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Speaker 1: Bloomberg Audio Studios, Podcasts, radio news. This is Bloomberg business

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Speaker 1: Week inside from the reporters and editors who bring you

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Speaker 1: America's most trusted business magazine, plus global business, finance and

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Speaker 1: tech news. The Bloomberg Business Week Podcast with Carol Messer

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Speaker 1: and Tim Stenebeck from Bloomberg Radio.

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Speaker 2: Carol Mappler, Tim's done live. You're in San Francisco at

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Speaker 2: the Bloomberg Technology Summit, and for to say, Carol, We've

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Speaker 2: already heard from the great lineup of speakers. I mentioned

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Speaker 2: Flows out of Newman formerly of We Work Lead, a caller,

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Speaker 2: the godmother of ai AI. Yeah, Culture Professor heard from

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Speaker 2: her a little earlier. Snapsil and Spiegel also here at

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Speaker 2: this event. Got a great lineup.

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Speaker 3: It's really fun to see the networking going on. We've

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Speaker 3: also got our own star and we're talking about Bradstone.

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Speaker 4: He is the editor of Bloomberg Business Week. He's also,

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Speaker 4: of course the author.

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Speaker 3: Of numerous books including those on Amazon, on Amazon, on Bound,

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Speaker 3: and so much more. Joining us here at the Bloomberg

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Speaker 3: Tech sum and actually you have invited us in, so

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Speaker 3: thank you so much for that. First of all, I

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Speaker 3: feel like there's so much going on AI. Yes, but

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Speaker 3: tell us about Brad. The conversations you guys had as

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Speaker 3: a team about what you wanted to cover there.

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Speaker 5: Yeah, you know, it's funny.

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Speaker 6: We do these every year and we tried not to

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Speaker 6: repeat ourselves, and last year we were really talking about

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Speaker 6: the emergence of chat GPT. It had gotten everybody talking,

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Speaker 6: and so of course we had Sam Altman as our

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Speaker 6: keynote speaker, and you can't buy the same people every year.

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Speaker 6: But the other thing that's happened is it has started

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Speaker 6: a movement, and so we thought, well, who else do

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Speaker 6: we want?

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Speaker 5: And the first names that's sprung to mind.

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Speaker 6: Are the founders of Anthropic Siblings.

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Speaker 5: Dario and Daniela Amoday.

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Speaker 6: They actually worked at the Open AI and spun out

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Speaker 6: maybe with an implicit objection to how open A was

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Speaker 6: being run. That's where we started today and it's really

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Speaker 6: set the tone for the conversations today mentioned faife A Lee,

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Speaker 6: We have been coaches, la Reid Hoffmann coming later. Really,

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Speaker 6: I think where we settled on was like, let's find

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Speaker 6: some of the architects of this moment and to ask

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Speaker 6: them the hard questions about are we really kind of

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Speaker 6: comfortable where we're going?

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Speaker 5: Or is this an arms race that is propelling everyone

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Speaker 5: forward just to win?

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Speaker 2: And what does that mean? So what does that mean?

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Speaker 2: Because you know, if we think about where we were

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Speaker 2: a year ago when everybody was playing with chat GPT.

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Speaker 2: GPT four had basically just come out a little over

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Speaker 2: a year ago, when you had the Tech summit, here,

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Speaker 2: where are we this year with the run up in video?

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Speaker 2: You have Amazon investing in anthrophic, you have all the

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Speaker 2: open high eigh drama that happened at the end of

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Speaker 2: last year. Where are we when it comes to sort

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Speaker 2: of like AI being in primetime this Yes.

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Speaker 5: I will answer with something that you will never hear

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Speaker 5: on the stage downstairs.

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Speaker 2: Okay, I don't know here and an.

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Speaker 6: Age of remarkable what I call AI ambiguity. There's so

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Speaker 6: many things we don't know. So for example, Meta has

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Speaker 6: open source the Lamaitrie model. What does that mean for

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Speaker 6: these other companies that have spent hundreds of millions maybe

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Speaker 6: seeing billions to train and operate a model. What does

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Speaker 6: it mean that you can now get the technology for free?

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Speaker 5: Will chatbots be a thing that we regularly use or are.

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Speaker 6: They a novel expression of the technology and then they'll

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Speaker 6: fade away?

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Speaker 5: Are their lanes for startups outside.

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Speaker 6: Of what all the big tech companies that have the

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Speaker 6: resources are doing with AI.

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Speaker 7: These are great questions. I actually don't think that anybody.

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Speaker 5: Has the answer.

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Speaker 6: I think that even when I think about social media

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Speaker 6: explosion or on demand apps fueled by the iPhone, that.

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Speaker 5: We sort of had a sense of where, Okay, well

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Speaker 5: this is cool and people will.

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Speaker 2: Find it useful.

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Speaker 6: And I just think everything from how will use it

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Speaker 6: to what the economics will look like are still completely

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Speaker 6: up in the air.

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Speaker 3: I love that you went there, because I do feel

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Speaker 3: like we've all been agog if you will, right on Nvidia.

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Speaker 4: And the chips and so and so forth coming off

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Speaker 4: of milk, and I just came and people.

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Speaker 3: To even are like trying to assess where do we

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Speaker 3: put allocate the money going forward? So much of what

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Speaker 3: I heard rod was data centers right and infrastructure build out.

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Speaker 3: Like people were still like, we haven't quite figured out

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Speaker 3: the applications.

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Speaker 4: Of AI going forward.

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Speaker 5: I think that's true.

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Speaker 6: Where where I'm sort of bullish and why don't you

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Speaker 6: let's see us an example of the good old Bloomberg terminal,

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Speaker 6: where you really need to know how to get information

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Speaker 6: from the terminal, right, we know a whole lexicon and

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Speaker 6: that's pretty similar to other computer systems. They they'll do

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Speaker 6: what you tell them to do, and often you need

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Speaker 6: to speak a particular language, maybe even coding.

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Speaker 7: And what AI is.

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Speaker 6: I think of it as like a new user interface

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Speaker 6: where you can express yourself either with a voice command

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Speaker 6: or a written command and a plain language and have

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Speaker 6: it respond.

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Speaker 2: And have it do things for you.

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Speaker 6: I think that is like and I said, we're going

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Speaker 6: to see an IO next week with Google and WWWC, WWDC,

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Speaker 6: with Apple them baking it into their devices and basically

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Speaker 6: creating a new way to use technology.

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Speaker 2: Is it too early to say, Brad, that we're in

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Speaker 2: an era right now like we were with the web

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Speaker 2: browser in the nineties, sort of the dawn of the Internet,

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Speaker 2: you know, tech bubble. Notwithstanding, but the idea that there

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Speaker 2: was this new technology that fundamentally changed not just the

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Speaker 2: way we live, but the way we interact with each

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Speaker 2: other and the way that we spend money.

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Speaker 6: I'm going to go back to my handy AI ambiguity.

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Speaker 7: And say, I don't know.

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Speaker 5: I mean, if if this is the.

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Speaker 6: Domain of only the biggest companies that can afford it,

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Speaker 6: and their proxies like Anthropic that maybe it isn't a

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Speaker 6: giant ce that lists all boats.

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Speaker 5: Maybe you know, this is going to be.

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Speaker 6: Owned by four or five companies and governments around the world,

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Speaker 6: and it will concentrate their power. The arrival of the

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Speaker 6: web browser was the arrival of all sorts of entrepreneurial opportunity.

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Speaker 6: To point, you know, we had a rise and then

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Speaker 6: a crash, and then a rise again. I don't think

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Speaker 6: it's clear that this is ready set go for everyone.

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Speaker 6: It's very expensive to run these models. They'll maybe regulated,

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Speaker 6: as fafe Lea said earlier today. If you regulate them,

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Speaker 6: you could create a powerful black market based on open

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Speaker 6: source models.

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Speaker 5: So all sorts of sort of unresolved issues. And I'm

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Speaker 5: just not sure.

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Speaker 6: Despite the fact that you go to y Combinator, any

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Speaker 6: startup community here and all you will see and hear

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Speaker 6: about are AI startups.

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Speaker 5: I'm just not sure how big the opportunity is going

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Speaker 5: to be.

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Speaker 2: We'll see.

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Speaker 3: Hey, Adam Newman, you know you think about cycles right

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Speaker 3: in technology, and we're trying to still figure out right

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Speaker 3: the AI cycle going forward. But how what did you

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Speaker 3: want to know or what you know, Adam Newman in

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Speaker 3: terms of you think about we were what how it

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Speaker 3: was a cover of Bloomberg.

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Speaker 4: Business Week, maybe.

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Speaker 3: Several times I can't remember before Brad's time, before Brad's time,

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Speaker 3: but nonetheless it was a great story.

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Speaker 4: We talked so much about that company and where it

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Speaker 4: was going, and obviously it's gone through.

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Speaker 7: Well.

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Speaker 6: First of all, I felt so privileged to be able

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Speaker 6: to bring his particular brand of charismatic lunacy.

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Speaker 5: Uh oh, you know that's I don't mean.

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Speaker 6: In a harsh way. You know, he's entertaining as heck. Yeah,

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Speaker 6: like he exudes charisma. He's at it again.

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Speaker 5: I wanted.

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Speaker 6: He's trying to buy where you work out of bankruptcy.

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Speaker 6: So that was the first topic. He he they've they've

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Speaker 6: not taken his bid, and he's clearly signaling that he's

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Speaker 6: going to appeal that they don't. They think that the

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Speaker 6: creditors should have looked at his bid more carefully, that

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Speaker 6: he had the higher bid. He said that the current

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Speaker 6: creditors who won the deal are making promises they can't keep.

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Speaker 6: So that was the first topic. Then we asked him

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Speaker 6: up flow his residential real estate company.

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Speaker 7: You know, this idea of.

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Speaker 6: Spending hundreds of millions of dollars to acquire properties and

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Speaker 6: then not just be a landlord, but to use technology

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Speaker 6: to create community and a sense of belonging. Him bringing

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Speaker 6: his classic sense of uh, you know, quasi mysticism into

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Speaker 6: the world of business.

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Speaker 5: And I asked, I was like, why do you keep

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Speaker 5: we were, Yeah, why do you have that?

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Speaker 8: You know?

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Speaker 5: But it's who he is and he you know, he's.

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Speaker 6: Somebody who speaks to the heart. I asked him a

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Speaker 6: little bit. I wanted to know about whether he was different,

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Speaker 6: and I really didn't do get the sense of one,

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Speaker 6: he's got a confrontational board now that's gonna say no

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Speaker 6: to him.

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Speaker 5: He didn't have that before.

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Speaker 4: And that too, he's there's an adult, a couple of

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Speaker 4: adults in the glorio.

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Speaker 5: There were before, but they weren't acting like it.

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Speaker 6: And that he even said that on his latest proposal.

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Speaker 6: Mark Andresam told him no, Yeah, that's interesting. I'm not

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Speaker 6: sure Masayoshi Son ever told him no. And then the

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Speaker 6: third thing was I wanted to know from him how

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Speaker 6: he was different as a CEO. And he mentioned the board,

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Speaker 6: and he also mentioned the advice that Jeff Bezos had

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Speaker 6: given him to always talk last in a meeting because

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Speaker 6: otherwise you're gonna kind of pollute the water and sway

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Speaker 6: people's opinion.

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Speaker 5: And I said, are you able to follow that advice

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Speaker 5: to go well?

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Speaker 6: And his CFO was sitting in the audience and gave

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Speaker 6: us a kind of half hearted thumbs up.

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Speaker 5: So maybe something to work on.

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Speaker 2: So do you see him pulling off that we work

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Speaker 2: out of bankruptcy.

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Speaker 5: I don't know if he will win that deal. I

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Speaker 5: think that.

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Speaker 6: You know, the creditors are are required and it is

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Speaker 6: in their personal interest to get the most money for

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Speaker 6: the death that exists and to bring the company out

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Speaker 6: of the bankruptcy. And the fact that they've chosen this

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Speaker 6: other path suggests to me, and I haven't really looked

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Speaker 6: at it, but that it may be a more complicated

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Speaker 6: picture that Adam has presented and that it may not

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Speaker 6: only be about a popularity contest. And I also asked

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Speaker 6: him to explain to me why we work should be

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Speaker 6: in flow and whether it was just a personal mission

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Speaker 6: of redemption.

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Speaker 5: And I wasn't sure.

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Speaker 6: I really believe, you know, fully believed that those two

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Speaker 6: companies belong together and that it wasn't a little emotional

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Speaker 6: for him.

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Speaker 5: In fact, he said that it.

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Speaker 8: Was so.

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Speaker 6: You know, and maybe there are personal reasons the quest

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Speaker 6: that they don't want to resell we work to Adam Newman.

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Speaker 5: We can all imagine what they are. He wasn't you know?

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Speaker 6: He the company had problems under his leadership, and the

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Speaker 6: question is are those personal reasons do they meet a

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Speaker 6: valid legal test?

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Speaker 3: You know, it's fascinating think about the pandemic and how

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Speaker 3: we're rethinking how we work and what kind of office space.

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Speaker 4: Is ultimately needed.

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Speaker 3: Like his thinking about this concept of having flexible workspace

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Speaker 3: like as you need.

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Speaker 4: It wasn't so far fetched. It's just timing and maybe execution.

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Speaker 6: And I mean I would say this, I think that

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Speaker 6: the history of real estate is and you know, Donald

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Speaker 6: Trump has exhibit A is a is a history of

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Speaker 6: ups and downs, bubbles and crashes.

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Speaker 5: We're seeing in China right now.

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Speaker 6: Business Week we did a list of all the billionaires

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Speaker 6: that have lost everything betting on the Chinese.

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Speaker 5: So that's no different.

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Speaker 6: But you know the difference is he voted a little higher,

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Speaker 6: wrapped in a mantle of being a technology company and

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Speaker 6: trying to take a public as a tech company, and

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Speaker 6: then wrote it even lower because investors ultimately.

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Speaker 2: Didn't buy the visual.

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Speaker 4: Can I talk about.

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Speaker 7: Robot?

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Speaker 6: Yes, this is a humanoid robot developed by Engineered Arts,

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Speaker 6: a company in Cornwall, England. I'm about to go in

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Speaker 6: a either a career high or a career low. I'm

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Speaker 6: going to go on stage and interview the robot.

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Speaker 2: So it's going to answer your questions in real time.

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Speaker 6: Yes, yeah, yeah, it's a creepily human like It speaks

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Speaker 6: and I presume should be addressed as a female.

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Speaker 7: That's a choice.

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Speaker 5: That's a choice, and it's really remarkably I wouldn't say it's.

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Speaker 7: Lifelike, but because I did address it, I did some tests.

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Speaker 6: It's expressive and it's design to make people feel comfortable

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Speaker 6: with it. It's very it's to me, even though it's

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Speaker 6: a British project, it very much reminds me of the

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Speaker 6: Japanese bipedal robots or the past that Sony and Samsung

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Speaker 6: and they were always working on them. The Japanese love

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Speaker 6: their their bipedal robots.

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Speaker 2: It's certainly like it's it's incredible novelty. But we should

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Speaker 2: know that there are lots of companies working on robots

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Speaker 2: like this to help in warehouses. Think about what it

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Speaker 2: would you do a company like Amazon to be able

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Speaker 2: to have you.

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Speaker 7: Know, this am a coon.

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Speaker 6: I hope at least does not move because if you

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Speaker 6: see me running across the stam, I don't know, uh

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Speaker 6: that it's an ambulatory Uh yeah, now I could ask

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Speaker 6: and it's running chat GPT for Turbo.

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Speaker 9: So I think that's.

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Speaker 2: Interesting that, So explain what that means.

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Speaker 6: Like it's essentially that engineered arts has developed the machine,

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Speaker 6: but the brains are borrowed using the API of open.

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Speaker 5: AI and chat GPT.

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Speaker 6: Well that's interesting going forward that maybe robot makers don't

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Speaker 6: need to worry as much about main general intelligence, but

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Speaker 6: they get hooked into one of these large models, use

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Speaker 6: the API, and they can they can be more engineering

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Speaker 6: companies that think about things like how the joints work,

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Speaker 6: how balance works, as you mentioned Caro, all the expressiveness

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Speaker 6: of the robots.

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Speaker 2: So what does that mean she's note connected to the

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Speaker 2: cloud right now?

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Speaker 7: Oh?

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Speaker 5: I would imagine that she is. Okay, yeah, well we'll

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Speaker 5: see if Wi fi goes down.

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Speaker 2: Answer to make sure she's on the right network. Okay,

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Speaker 2: I was thinking and I were talking.

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Speaker 4: About the hotel. We're saying we're not gonna say where

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Speaker 4: it is, but.

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Speaker 3: It's like, right, we couldn't get anybody I answer, So

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Speaker 3: we could have used Amica to like answer our phone

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Speaker 3: call and like help us answer some question.

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Speaker 2: There is the conference, which is like huge, Well that's

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Speaker 2: the thing.

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Speaker 7: It's like you would need Amica to do that.

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Speaker 6: You would need an Alexa or chatch BT, right, and

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Speaker 6: then and then robots can just be these physical things.

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Speaker 6: And and the question is do we really need a

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Speaker 6: buy pedal robot that's trying to trick us into being

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Speaker 6: thinking that it's human or will they be Timmy mentioned

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Speaker 6: industrial robots, you know, little things that do very specific tasks,

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Speaker 6: deliver the mail, deliver packages, that kind of thing.

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Speaker 3: Hey, can I ask you what's the biggest I don't know,

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Speaker 3: I know AI like as you go through today, like

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Speaker 3: what are you looking for a question to be answered?

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Speaker 3: Or something like I think you know so much this world,

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Speaker 3: there's something top of.

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Speaker 7: Mind for you.

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Speaker 5: I mean, I think we all want to know, like

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Speaker 5: where it's going. I guess specifically, we've seen it.

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Speaker 6: Since the September of twenty twenty two, every successive models

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Speaker 6: of chat GBT or any of these other.

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Speaker 5: Chatbots has been step change improvements.

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Speaker 6: Fed by more and more data and more and more

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Speaker 6: processing and power and money running on in video chips.

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Speaker 5: So my question is is that going to continue or are.

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Speaker 6: We hitting a natural limit, either in the money that

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Speaker 6: we could spend, the train, the available processors, or or

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Speaker 6: just the model matches out all right and it's not

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Speaker 6: giving us step change improvements anymore.

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Speaker 4: Good stuff from Bradstone.

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Speaker 3: As always, We've got more, including we're to continue with AI.

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Speaker 2: Folks, this is Bloomberg.

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Speaker 1: You're listening to the Bloomberg Business Week podcast. Catch us

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Speaker 1: Live weekday afternoons from two to five pm Eastern Listen

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Speaker 1: on Apple.

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Speaker 2: Car Play and then Broyt Auto with.

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Speaker 1: A Bloomberg Business act or want us live on YouTube?

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Speaker 2: Still give value? No question buzzing about AI. We've been

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Speaker 2: talking about it. But venture capital dealmaking, Carol, it's still suppressed.

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Speaker 2: I got some stats here. Here's some numbers. Okay here

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Speaker 2: in the US VC's thirty six point six billion into

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Speaker 2: startups in the first quarter of the year. That's according

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Speaker 2: to Pitchbook. The funding went to twenty eight hundred companies.

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Speaker 2: On both measures, it's a nearly thirty percent decrease compared

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Speaker 2: with the same period in twenty twenty throws three. So

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Speaker 2: the takeaways startups had their worst funding your last year

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Speaker 2: since twenty nineteen. This year it's starting off even worse.

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Speaker 3: So despite of the excitement, Well, we've got a great

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Speaker 3: boys to really talk to us about the startup community.

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Speaker 4: Alien Lee is with us.

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Speaker 3: She's the founder and managing partner of the VC firm

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Speaker 3: Cowboy Ventures. They invest mostly in software companies based here

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Speaker 3: in the US. We should say that her firm has

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Speaker 3: made more than one hundred early stage investments, specializing in

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Speaker 3: seed rounds.

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Speaker 4: She's on site with us. The other thing is she

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Speaker 4: has done the data analysis.

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Speaker 3: That is the reason we use the word unicorn when

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Speaker 3: it comes to startups.

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Speaker 4: We love data here at Bloomberg.

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Speaker 5: How are you.

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Speaker 4: I am so happy to be here. We'll tell us

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Speaker 4: about the VC world, Like give us an idea. It's

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Speaker 4: a great indicator in terms of the health of the economy.

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Speaker 8: So we just finished a panel myself, Kirsten Green and

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Speaker 8: auDA from Base ten with Tom Giles, who's the tech

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Speaker 8: editor for Bloomberg Tech I.

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Speaker 4: We talked about what's going on.

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Speaker 8: So, like you mentioned, we had a big slowdown last year.

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Speaker 8: A lot of people put their pencils down. They're both

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Speaker 8: like it felt like there was a falling night in

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Speaker 8: terms of public markets, which obviously affects the private markets,

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Speaker 8: and so people said it's not a great time to

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Speaker 8: be investing in private companies.

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Speaker 4: I do think that we're.

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Speaker 8: Seeing quite a pick up this year, but it's largely

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Speaker 8: driven by AI, which is great. It's very exciting. It

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Speaker 8: is the new hotness in venture capital. So you just

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Speaker 8: have to It can be quite treacherous, I think to

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Speaker 8: invest in AI right now because it's you know, obviously

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Speaker 8: there's a lot of energy right now. We're kind of

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Speaker 8: in a consolidation phase, and for very good reasons, right

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Speaker 8: with whether it's Microsoft or Meta or Salesforce or Service.

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Speaker 7: Now or Google.

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Speaker 8: You need a lot of open AI, you need a

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Speaker 8: lot of money, you need a lot of GPU, you

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Speaker 8: need a lot of engineers, and so there's kind of

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Speaker 8: this like techtonic plate war going on in the big

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Speaker 8: tech players right now, and so if you've got a

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Speaker 8: really big fund, you could try and align your Obviously,

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Speaker 8: most of these companies are public companies right so right

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Speaker 8: it's a great time for public Nvidia and to be

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Speaker 8: a public markets investor to be able to get exposure

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Speaker 8: to like the AI that is going to be powering.

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Speaker 8: I think most applications for decades to come. But for

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Speaker 8: private markets, I think there's just a lot of money

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Speaker 8: going into startups, many of whom are.

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Speaker 5: Not going to make it.

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Speaker 2: So the thing that's interesting about this, and Brad kind

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Speaker 2: of alluded to him, we were talking to him, is

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Speaker 2: the idea of it's so expensive to have this processing

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Speaker 2: power to buy these GPOs. Yeah, and it's not as

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Speaker 2: easy as you know, ten fifteen years ago creating an

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Speaker 2: app yeah, and building a network, yeah, which was sort

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Speaker 2: of what we saw venture capital support in that generation.

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Speaker 7: Yeah.

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Speaker 9: Is there a concern that you.

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Speaker 2: Know, you do early stages, yeah investing, you do seed startups,

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Speaker 2: your seed investing. Is there a concern that these companies

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Speaker 2: just can't raise the capitol in order to subsport them.

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Speaker 8: So there are large models that the big technology companies

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Speaker 8: are investing in that have billions of parameters that you

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Speaker 8: need billions of dollars for GPOs. We have been investing

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Speaker 8: at Cowboy Ventures, as you mentioned, we're seed stage investors.

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Speaker 8: Our first AI driven enterprise software companies we invested in

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Speaker 8: eight years ago, where it's they use what we call

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Speaker 8: small model where they don't need.

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Speaker 4: To buy GPUs.

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Speaker 8: They basically get a proprietary data set. In that case,

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Speaker 8: the company's called text You, and we basically got HR

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Speaker 8: related documents, jobs, pecs, performance reviews, and we train the

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Speaker 8: AI using very situational specific parameters to basically understand how

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Speaker 8: it can help people write performance reviews more effectively, how

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Speaker 8: it can help people write job specs more effectively. And

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Speaker 8: you just don't need the giant GPU to be able

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Speaker 8: to do that work. And then also a lot of

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Speaker 8: it is around how you solve the problem for everyday

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Speaker 8: business users instead of them using Google Docs or word

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Speaker 8: you need to figure out how would I get into

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Speaker 8: their workflow, whether it's through workday or lattice, and how

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Speaker 8: do I actually create those integrations so that the way

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Speaker 8: that the AI can be in the business process of

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Speaker 8: every day people inside the enterprise. And so I think

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Speaker 8: that's those kinds of business or industry specific applications.

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Speaker 4: You don't need billions of dollars to build those applications.

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Speaker 4: But is that scalable Okay, I'm listening to you. Is

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Speaker 4: that scalable elsewhere elsewhere? Meaning other industries like that mod completely.

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Speaker 8: So we're seeing I think a lot of what's the

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Speaker 8: app there's the application layer and there's infrastructure, right, so

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Speaker 8: there's money going into both at the application layer.

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Speaker 4: That's where a lot of VC funds are playing right now.

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Speaker 8: And it makes a lot of sense because so many

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Speaker 8: people in whether it's the food and beverage industry, in hospitals,

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Speaker 8: in education, people are still using pen paper spreadsheets. They're

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Speaker 8: not actually leveraging the underlying data, and the applications that

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Speaker 8: use every day are not smart. And so I think

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Speaker 8: there's pretty much every industry and every application that we

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Speaker 8: look at today is going to have new applications that

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Speaker 8: are powered by AI.

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00:20:46,800 --> 00:20:48,400
Speaker 4: So it is a tremendous opportunity.

449
00:20:48,600 --> 00:20:51,560
Speaker 2: Okay, I'm speaking of opportunities. I'm wondering what's more difficult

450
00:20:51,760 --> 00:20:54,280
Speaker 2: in this world where cash can earn four and a

451
00:20:54,320 --> 00:20:59,440
Speaker 2: half to five per tails. That's an opportunity. Yeah, what's

452
00:20:59,520 --> 00:21:00,000
Speaker 2: harder right now?

453
00:21:00,119 --> 00:21:00,239
Speaker 10: Now?

454
00:21:00,480 --> 00:21:02,760
Speaker 2: Is it harder to go raise money from investors to

455
00:21:02,840 --> 00:21:05,320
Speaker 2: then deploy yeah, or is it harder to find the

456
00:21:05,400 --> 00:21:07,400
Speaker 2: companies to deploy that money into.

457
00:21:08,560 --> 00:21:11,400
Speaker 8: We're very fortunate, you know, we're pretty disciplined. Fun We've

458
00:21:11,400 --> 00:21:13,440
Speaker 8: been around for over ten years now. We've had pretty

459
00:21:13,480 --> 00:21:16,080
Speaker 8: much the same investors or what we call LP Limited

460
00:21:16,119 --> 00:21:18,399
Speaker 8: Partners for pretty much the ten years, so we're very

461
00:21:18,480 --> 00:21:22,640
Speaker 8: fortunate and they are savvy investors who understand the upside

462
00:21:22,680 --> 00:21:25,240
Speaker 8: of investing in venture capital firms and they know it's

463
00:21:25,240 --> 00:21:28,320
Speaker 8: a ten to fifteen year investment. So fortunately for us,

464
00:21:28,560 --> 00:21:31,200
Speaker 8: it's definitely gotten harder, and it's I think in many

465
00:21:31,240 --> 00:21:34,160
Speaker 8: ways it's good because it holds us very accountable. It's

466
00:21:34,240 --> 00:21:36,280
Speaker 8: like there are benchmarks that you have to beat to

467
00:21:36,359 --> 00:21:38,320
Speaker 8: be worth investing in, and that's our job is to

468
00:21:38,359 --> 00:21:41,000
Speaker 8: make sure that we beat those benchmarks fund after fun

469
00:21:42,520 --> 00:21:45,440
Speaker 8: and also we raise every three years or every four years,

470
00:21:45,520 --> 00:21:47,800
Speaker 8: so you know, you raise the fund and then you

471
00:21:47,880 --> 00:21:50,080
Speaker 8: have three years to basically go find some great investments.

472
00:21:50,119 --> 00:21:53,040
Speaker 8: So that's the it's gotten quite competitive in private markets.

473
00:21:53,200 --> 00:21:55,280
Speaker 8: A ton of money flooded in in twenty twenty, twenty

474
00:21:55,320 --> 00:21:57,399
Speaker 8: twenty one, and it's probably not going to it's not

475
00:21:57,480 --> 00:21:59,920
Speaker 8: going to recede, and so there's just more money chase

476
00:22:00,000 --> 00:22:01,440
Speaker 8: saying private companies than ever.

477
00:22:01,760 --> 00:22:03,840
Speaker 3: One thing I wanted to ask you, Aleen, is what's

478
00:22:04,280 --> 00:22:08,119
Speaker 3: with all the attention on AI where what's being neglected

479
00:22:08,200 --> 00:22:10,240
Speaker 3: maybe in the venture world or where do you think

480
00:22:10,320 --> 00:22:12,200
Speaker 3: we should also be thinking about when it comes to

481
00:22:12,359 --> 00:22:13,200
Speaker 3: tech innovation.

482
00:22:13,840 --> 00:22:16,640
Speaker 4: I mean, I do think, oh, that's a really good question.

483
00:22:18,800 --> 00:22:21,760
Speaker 4: I think the big boom when interest.

484
00:22:21,600 --> 00:22:24,480
Speaker 8: Rates were basically zero in twenty twenty and twenty twenty one,

485
00:22:25,119 --> 00:22:28,320
Speaker 8: it brought so much attention to the venture capital and

486
00:22:28,359 --> 00:22:31,560
Speaker 8: technology world that actually what we would have considered long

487
00:22:31,680 --> 00:22:36,720
Speaker 8: tail markets parking garages, like companies selling software to parking garages.

488
00:22:36,320 --> 00:22:36,840
Speaker 4: Got funded.

489
00:22:36,960 --> 00:22:39,880
Speaker 8: Like the Unicorn analysis that we did in twenty thirteen

490
00:22:40,440 --> 00:22:43,879
Speaker 8: versus twenty twenty three showed a huge difference in the

491
00:22:43,920 --> 00:22:47,840
Speaker 8: amount of money that's going to industry specific software whether

492
00:22:47,920 --> 00:22:50,520
Speaker 8: and so in the original Unicorn analysis in twenty thirteen,

493
00:22:50,520 --> 00:22:52,439
Speaker 8: it was actually, it's quite interesting how it flip flopped

494
00:22:52,600 --> 00:22:56,760
Speaker 8: eighty percent consumer companies and twenty percent enterprise software companies.

495
00:22:57,160 --> 00:22:59,480
Speaker 8: Ten years later it foot flopped and became eighty percent

496
00:22:59,640 --> 00:23:03,640
Speaker 8: enterprise twenty percent consumer. And so in those enterprise you've

497
00:23:03,640 --> 00:23:07,320
Speaker 8: got climate tech companies, you've got agriculture companies, you've got

498
00:23:07,520 --> 00:23:11,440
Speaker 8: companies serving parking garages. So entrepreneurs especially, I think since

499
00:23:11,480 --> 00:23:14,600
Speaker 8: the iPhone debuted in two thousand and seven, pretty much

500
00:23:14,880 --> 00:23:18,240
Speaker 8: everyone in the modern world understands the power of technology,

501
00:23:18,600 --> 00:23:21,760
Speaker 8: and so if they work in a coord if they

502
00:23:21,880 --> 00:23:25,760
Speaker 8: work in at a nursing station, they understand how much

503
00:23:25,800 --> 00:23:27,920
Speaker 8: inefficiency there is and how much opportunity there is for

504
00:23:28,040 --> 00:23:30,600
Speaker 8: technology to actually reinvent the way they do things and

505
00:23:30,760 --> 00:23:31,600
Speaker 8: let humans.

506
00:23:31,359 --> 00:23:34,080
Speaker 4: Do what they do best better. Waiting for health care

507
00:23:34,760 --> 00:23:36,120
Speaker 4: to really embrace.

508
00:23:35,800 --> 00:23:39,800
Speaker 2: Technology, keep waiting. You know, if you need give you a.

509
00:23:39,880 --> 00:23:42,040
Speaker 4: Chip information, I'm ready to do that.

510
00:23:42,160 --> 00:23:43,800
Speaker 2: I actually am. If you need a copy of the

511
00:23:43,960 --> 00:23:47,600
Speaker 2: X ray, here's a CD ROW. I'm not joking. Earlier today,

512
00:23:47,840 --> 00:23:50,639
Speaker 2: that's still the way like ten back then.

513
00:23:50,640 --> 00:23:54,560
Speaker 8: Ode posts Let's speak earlier today with Emily Chang, and

514
00:23:54,640 --> 00:23:57,720
Speaker 8: he was saying that the thing that he's going to

515
00:23:57,760 --> 00:24:02,040
Speaker 8: tell President Biden tomorrow is that every person should have

516
00:24:02,160 --> 00:24:05,080
Speaker 8: a personal doctor and every case to have a personal

517
00:24:05,119 --> 00:24:08,080
Speaker 8: tudor tutor, and that that would reinvent both education and

518
00:24:08,280 --> 00:24:09,679
Speaker 8: lighters and it.

519
00:24:09,680 --> 00:24:10,280
Speaker 4: Would be amazing.

520
00:24:11,760 --> 00:24:15,600
Speaker 1: You're listening to the Bloomberg Business Week podcast. Listen live

521
00:24:15,720 --> 00:24:18,520
Speaker 1: each weekday starting at two pm Eastern on Apple car

522
00:24:18,640 --> 00:24:21,560
Speaker 1: Play and Android Auto with the Bloomberg Business Ad. You

523
00:24:21,640 --> 00:24:24,880
Speaker 1: can also listen live on Amazon Alexa from our flagship

524
00:24:24,960 --> 00:24:29,040
Speaker 1: New York station, Just Say Alexa, playing Bloomberg eleven thirty.

525
00:24:30,480 --> 00:24:33,639
Speaker 2: Well Tick Ventures is a free seed and seed venture

526
00:24:33,680 --> 00:24:37,040
Speaker 2: capital firm. It focuses on investing in demographic changes that

527
00:24:37,160 --> 00:24:40,360
Speaker 2: are driven by tomorrow's Internet users. So what that means

528
00:24:40,359 --> 00:24:42,440
Speaker 2: as far as where they put their money. There's a

529
00:24:42,440 --> 00:24:44,639
Speaker 2: company out there called Raars where you can invest in

530
00:24:44,680 --> 00:24:49,200
Speaker 2: sneakers and collectibles. Right. It's an aipowered workforce training platform,

531
00:24:49,640 --> 00:24:52,160
Speaker 2: and then it comes to be behavioral health. The platform

532
00:24:52,280 --> 00:24:55,160
Speaker 2: is called it most days. That's among other investments ADMD.

533
00:24:55,320 --> 00:24:56,600
Speaker 4: I love the idea of demographics.

534
00:24:56,640 --> 00:24:58,359
Speaker 3: I really think it's kind of figure out what are

535
00:24:58,400 --> 00:25:01,040
Speaker 3: the trends not only today but really more importantly up

536
00:25:01,040 --> 00:25:03,960
Speaker 3: tomorrow with us right now is Monique Woodward. She's founding

537
00:25:04,040 --> 00:25:07,360
Speaker 3: partner managing partner at Cake Vners. Welcome, Welcome, so nice

538
00:25:07,400 --> 00:25:08,159
Speaker 3: to be talking with you.

539
00:25:08,440 --> 00:25:09,639
Speaker 4: Thank you, thanks for having me.

540
00:25:09,960 --> 00:25:12,080
Speaker 3: When you think about demographics, right, so you can dice

541
00:25:12,119 --> 00:25:14,200
Speaker 3: and slice that, we make so many different ways. What

542
00:25:14,280 --> 00:25:16,399
Speaker 3: are the demographics that you really like to focus on

543
00:25:16,520 --> 00:25:17,520
Speaker 3: in today's environment?

544
00:25:18,240 --> 00:25:21,440
Speaker 11: So the Cake Venture's thesis around demographics is really based

545
00:25:21,480 --> 00:25:24,560
Speaker 11: on a few different layers. The first layer is aging

546
00:25:24,600 --> 00:25:28,320
Speaker 11: and longevity. There's ten thousand people turning sixty five every

547
00:25:28,400 --> 00:25:31,439
Speaker 11: single day in this country alone. And there's this massive

548
00:25:31,480 --> 00:25:34,480
Speaker 11: opportunity to help them use technology to make their lives

549
00:25:34,560 --> 00:25:36,960
Speaker 11: better as a age. The second layer of the cake

550
00:25:37,080 --> 00:25:38,840
Speaker 11: is the increased spending power of women and how the

551
00:25:38,920 --> 00:25:42,720
Speaker 11: female dollar drives companies to billion dollar plus outcomes. And

552
00:25:42,800 --> 00:25:45,400
Speaker 11: then the third is the shift to majority minority, where

553
00:25:45,440 --> 00:25:49,320
Speaker 11: people of color as a broad group, primarily Latino, Asian

554
00:25:49,359 --> 00:25:52,119
Speaker 11: and Black, becoming majority in the United States, are already

555
00:25:52,160 --> 00:25:53,000
Speaker 11: a global majority.

556
00:25:53,720 --> 00:25:56,359
Speaker 4: You know, it's really fascinating. I keep talking about this.

557
00:25:56,560 --> 00:25:58,080
Speaker 4: I was just back from Milkin and I had to

558
00:25:58,680 --> 00:26:01,800
Speaker 4: moderate a panel. It was called Thriving in Distruction.

559
00:26:02,080 --> 00:26:04,560
Speaker 2: Oh, you did several panels, but what was.

560
00:26:04,560 --> 00:26:05,479
Speaker 4: Interesting on our pref call.

561
00:26:05,560 --> 00:26:07,720
Speaker 3: So here I have somebody from real estate, from venture,

562
00:26:08,200 --> 00:26:12,000
Speaker 3: private equity guide, private credit guide, somebody who's in, you know,

563
00:26:12,119 --> 00:26:14,760
Speaker 3: trading every day on a platform over at Citadel Securities,

564
00:26:15,119 --> 00:26:18,760
Speaker 3: and what they wanted to talk about was longevity and

565
00:26:18,880 --> 00:26:21,040
Speaker 3: the aspect of how it's going to impact things like

566
00:26:21,119 --> 00:26:24,679
Speaker 3: pension app obligations, how we live, the kind of housing

567
00:26:24,760 --> 00:26:27,120
Speaker 3: we need, you know, whether we bring older people back

568
00:26:27,160 --> 00:26:27,960
Speaker 3: into the workforce.

569
00:26:28,560 --> 00:26:29,880
Speaker 4: What are the important.

570
00:26:29,480 --> 00:26:31,840
Speaker 3: Conversations that you think we need to be thinking about

571
00:26:31,880 --> 00:26:34,560
Speaker 3: when it comes to longevity and how that shapes specifically

572
00:26:34,600 --> 00:26:35,280
Speaker 3: your investments.

573
00:26:35,800 --> 00:26:38,800
Speaker 11: I think the big conversation right now is around how

574
00:26:38,840 --> 00:26:39,720
Speaker 11: do we live longer?

575
00:26:40,119 --> 00:26:40,239
Speaker 3: Right?

576
00:26:40,320 --> 00:26:42,800
Speaker 4: But I think that's just touching the surface of longevity.

577
00:26:43,080 --> 00:26:44,679
Speaker 4: It's really about how do we use.

578
00:26:44,560 --> 00:26:49,280
Speaker 11: Technology to give people better lives at older ages as

579
00:26:49,359 --> 00:26:52,040
Speaker 11: they get older, right, how do we use technology to

580
00:26:52,240 --> 00:26:55,480
Speaker 11: enhance their lives, help them age in place without moving

581
00:26:55,560 --> 00:26:59,040
Speaker 11: into a nursing home or retirement home, And how do

582
00:26:59,119 --> 00:27:04,120
Speaker 11: we give them better lies after sixty as they had

583
00:27:04,160 --> 00:27:05,720
Speaker 11: when they were in their twenties and thirties.

584
00:27:06,600 --> 00:27:08,359
Speaker 2: I'm wondering about the third theme that you touched on

585
00:27:08,600 --> 00:27:11,960
Speaker 2: in terms of demographics, the idea of investments in companies

586
00:27:12,680 --> 00:27:14,760
Speaker 2: that are not necessarily geared toward people of color. But

587
00:27:15,320 --> 00:27:18,320
Speaker 2: take this demographic into account. Here we are in Silicon Valley,

588
00:27:18,640 --> 00:27:20,760
Speaker 2: not necessarily the most diverse place when it comes to

589
00:27:20,960 --> 00:27:23,600
Speaker 2: where venture capital is deployed. I think that's fair to say,

590
00:27:24,359 --> 00:27:26,440
Speaker 2: how do you think about that in terms of where

591
00:27:26,480 --> 00:27:28,639
Speaker 2: you're putting your money in terms of the opportunities that

592
00:27:28,720 --> 00:27:31,320
Speaker 2: are out there? Because I think a big criticism that

593
00:27:31,440 --> 00:27:34,160
Speaker 2: folks have about ventor capitalist is, you know, they're always

594
00:27:34,200 --> 00:27:37,040
Speaker 2: just trying to solve problems that they have, Like, you know,

595
00:27:37,680 --> 00:27:39,520
Speaker 2: it's true, how do we get to tahoe faster? Right

596
00:27:39,760 --> 00:27:42,960
Speaker 2: to get to get a skew day out. I'm not

597
00:27:43,040 --> 00:27:45,159
Speaker 2: trying to be dismissive, but I mean it's like, you know,

598
00:27:45,280 --> 00:27:47,520
Speaker 2: it's like common criticism.

599
00:27:47,720 --> 00:27:49,399
Speaker 11: So one of the categories that I do a lot

600
00:27:49,440 --> 00:27:51,680
Speaker 11: of investing in it is the future of deskless work.

601
00:27:52,119 --> 00:27:55,240
Speaker 11: Eighty percent of the labor market is actually deskless work,

602
00:27:55,359 --> 00:27:57,639
Speaker 11: non office work, work that doesn't happen in an office,

603
00:27:58,000 --> 00:28:03,000
Speaker 11: health care jobs, construction jobs, service worker jobs. As you

604
00:28:03,080 --> 00:28:06,159
Speaker 11: might expect, a lot of these jobs over index in

605
00:28:06,240 --> 00:28:09,200
Speaker 11: women and people of color, and so I think that

606
00:28:09,480 --> 00:28:13,800
Speaker 11: AI and new technologies are going to be really important

607
00:28:14,240 --> 00:28:17,959
Speaker 11: in accelerating the adoption of those jobs, but also accelerating

608
00:28:18,200 --> 00:28:20,920
Speaker 11: the efficiency of those jobs. And so I think we're

609
00:28:20,960 --> 00:28:23,880
Speaker 11: at this really important inflection point where we can use

610
00:28:23,960 --> 00:28:27,320
Speaker 11: technology to improve the lives of healthcare workers, improve the

611
00:28:27,440 --> 00:28:31,159
Speaker 11: lives of service workers, and we haven't been doing it

612
00:28:31,359 --> 00:28:33,840
Speaker 11: because to your point, a lot of the people who

613
00:28:33,880 --> 00:28:37,520
Speaker 11: have been building for building technology that they come from

614
00:28:37,560 --> 00:28:40,840
Speaker 11: a knowledge worker background. So their immediate thought is to

615
00:28:40,920 --> 00:28:45,720
Speaker 11: build future work products software for knowledge workers. But these

616
00:28:45,960 --> 00:28:51,360
Speaker 11: massive opportunities have happen in healthcare and construction and manufacturing,

617
00:28:51,760 --> 00:28:53,520
Speaker 11: and those jobs are often held by.

618
00:28:53,480 --> 00:28:54,080
Speaker 7: People of color.

619
00:28:54,480 --> 00:28:57,480
Speaker 3: You know, I'm watching Bradstone up on stage with Amaica

620
00:28:57,720 --> 00:28:59,920
Speaker 3: the robot, and I'm thinking about in terms of how

621
00:29:00,200 --> 00:29:02,640
Speaker 3: care like whether or not there is some way.

622
00:29:02,800 --> 00:29:04,160
Speaker 4: I mean, one of the other trends that came.

623
00:29:04,080 --> 00:29:06,040
Speaker 3: Out of Milkan is that this shortage of health care

624
00:29:06,120 --> 00:29:08,960
Speaker 3: workers in particular, and I do wonder is there some

625
00:29:09,120 --> 00:29:12,840
Speaker 3: way in technology innovation that we can assist.

626
00:29:12,600 --> 00:29:15,440
Speaker 4: Those folks who actually have to do things.

627
00:29:15,280 --> 00:29:18,280
Speaker 3: In care for people, or whether because we're facing shortages

628
00:29:18,400 --> 00:29:19,680
Speaker 3: in a big way in that industry.

629
00:29:20,760 --> 00:29:23,160
Speaker 11: Well, we're already seeing it in companies like a Bridge,

630
00:29:23,600 --> 00:29:26,800
Speaker 11: a Bridge which raises series C in order to use

631
00:29:26,880 --> 00:29:31,760
Speaker 11: conversational as the conversations between healthcare workers and their patients.

632
00:29:31,720 --> 00:29:34,520
Speaker 4: And convert them into usable data.

633
00:29:35,360 --> 00:29:38,720
Speaker 11: So we're already seeing AI being really useful in the

634
00:29:38,800 --> 00:29:40,000
Speaker 11: lives of healthcare workers.

635
00:29:40,200 --> 00:29:40,320
Speaker 8: Right.

636
00:29:40,960 --> 00:29:44,600
Speaker 11: I'm very skeptical about the use of robotics in actual

637
00:29:44,720 --> 00:29:48,840
Speaker 11: patient to patient to healthcare worker interactions, but I do

638
00:29:49,040 --> 00:29:52,520
Speaker 11: think that AI is useful in freeing up the job

639
00:29:52,560 --> 00:29:55,440
Speaker 11: of the healthcare worker in order to make those patient

640
00:29:55,520 --> 00:29:59,720
Speaker 11: interactions much more robust, much more have a lot more depth.

641
00:30:00,200 --> 00:30:03,960
Speaker 11: And so I think we're far away from robots doing

642
00:30:04,080 --> 00:30:06,640
Speaker 11: that job, but I think we're we're in the AI

643
00:30:06,760 --> 00:30:07,000
Speaker 11: doing it.

644
00:30:07,200 --> 00:30:09,760
Speaker 2: I guess also maybe cut down on error too. When

645
00:30:09,800 --> 00:30:11,720
Speaker 2: you're tasked to a doctor. I mean, this is something

646
00:30:11,720 --> 00:30:14,120
Speaker 2: I recently noticed. When you're trying to describe like a

647
00:30:14,200 --> 00:30:16,440
Speaker 2: pain that you're having to a doctor, some sort of symptoms.

648
00:30:16,680 --> 00:30:19,760
Speaker 2: I mean, they're essentially serving the function of AI. What

649
00:30:19,840 --> 00:30:22,080
Speaker 2: they're doing is, you know, they're saying, Okay, well you

650
00:30:22,160 --> 00:30:24,360
Speaker 2: have this, this and this, here's your family history. They're

651
00:30:24,360 --> 00:30:26,760
Speaker 2: taking all these data points and they're trying to diagnose

652
00:30:26,800 --> 00:30:28,720
Speaker 2: you with something that you have. It's so easy to

653
00:30:28,800 --> 00:30:31,600
Speaker 2: see how that could be better served by something in

654
00:30:31,680 --> 00:30:32,320
Speaker 2: the cloud.

655
00:30:32,280 --> 00:30:35,520
Speaker 11: Exactly, or at least augmented in some way augmented. I

656
00:30:35,560 --> 00:30:37,640
Speaker 11: think that's what I think that's what we're looking for

657
00:30:37,840 --> 00:30:41,080
Speaker 11: within healthcare in particular. We are looking for technology to

658
00:30:41,280 --> 00:30:45,440
Speaker 11: augment the already great skills that these nurses, nurse practitioners,

659
00:30:45,520 --> 00:30:46,520
Speaker 11: and doctors already have.

660
00:30:46,960 --> 00:30:50,720
Speaker 4: We're not looking to replace them with robots, but give.

661
00:30:50,600 --> 00:30:53,280
Speaker 11: Them additional tools that make their jobs more efficient and

662
00:30:53,360 --> 00:30:55,640
Speaker 11: allow them to spend more about one on one time

663
00:30:55,720 --> 00:30:56,240
Speaker 11: with patients.

664
00:30:56,280 --> 00:30:59,400
Speaker 4: That's whole idea have adjacent right in terms of AI, Nick,

665
00:30:59,480 --> 00:31:01,360
Speaker 4: thank you so much. We really appreciate you coming by.

666
00:31:01,520 --> 00:31:03,360
Speaker 4: Thank you so much, fatterning time with our audience.

667
00:31:03,400 --> 00:31:05,000
Speaker 3: It's a lot of Nick Woodward g She is founding

668
00:31:05,040 --> 00:31:07,480
Speaker 3: partner and managing partner at Cake Ventures, joining us here

669
00:31:07,760 --> 00:31:09,560
Speaker 3: at the Bloomber of Technology Summit.

670
00:31:09,640 --> 00:31:13,480
Speaker 4: But these are some of the discussions and technologies like figuring.

671
00:31:13,160 --> 00:31:15,040
Speaker 3: Out what does it mean for our workplace? I mean,

672
00:31:15,080 --> 00:31:16,960
Speaker 3: you and I kind of joke about a kid about it.

673
00:31:17,000 --> 00:31:18,640
Speaker 3: But if we could get an assistant kind of writing

674
00:31:18,680 --> 00:31:19,400
Speaker 3: our scripts.

675
00:31:19,120 --> 00:31:21,480
Speaker 4: On otaily basis, I wouldn't mind that.

676
00:31:22,400 --> 00:31:24,160
Speaker 2: I was thinking about that a lot yesterday. I didn't

677
00:31:24,160 --> 00:31:27,920
Speaker 2: get when I was working. But to be fair, I mean,

678
00:31:27,960 --> 00:31:30,840
Speaker 2: you were literally, you know, ten hours of sleep in

679
00:31:30,920 --> 00:31:32,680
Speaker 2: the past four days because of what you were doing.

680
00:31:32,760 --> 00:31:34,160
Speaker 2: But the truth day is, there are a lot of

681
00:31:34,200 --> 00:31:37,120
Speaker 2: repetitive tasks out there. It would be great to see

682
00:31:37,160 --> 00:31:40,120
Speaker 2: some of those be replaced by machines of people. Look,

683
00:31:40,160 --> 00:31:42,920
Speaker 2: I oftentimes think about this conversation in the context of

684
00:31:43,200 --> 00:31:45,720
Speaker 2: other big historical shifts when it comes to the types

685
00:31:45,760 --> 00:31:47,800
Speaker 2: of work that people did one hundred years ago versus

686
00:31:47,880 --> 00:31:49,800
Speaker 2: the type of work we're doing now. Right, we don't

687
00:31:49,880 --> 00:31:51,600
Speaker 2: need a horse and buggy to get us from place

688
00:31:51,640 --> 00:31:54,680
Speaker 2: to place, you know. So what was replaced, you know?

689
00:31:54,760 --> 00:31:57,160
Speaker 2: And what did that allow people to do? What did

690
00:31:57,200 --> 00:31:58,600
Speaker 2: the steam engine allow people to do?

691
00:31:58,880 --> 00:31:58,920
Speaker 8: It?

692
00:31:59,040 --> 00:32:01,280
Speaker 2: Just does this thing stick the way that you know.

693
00:32:01,360 --> 00:32:02,000
Speaker 7: The proponents of it?

694
00:32:02,480 --> 00:32:04,000
Speaker 3: Even to go back that for I think about when

695
00:32:04,000 --> 00:32:06,000
Speaker 3: I started out in journalism, when I needed research, I

696
00:32:06,040 --> 00:32:08,280
Speaker 3: mean was on the phone with folks in the government,

697
00:32:08,440 --> 00:32:13,240
Speaker 3: like Commerce Department, can you FedEx overnight or send me research?

698
00:32:13,440 --> 00:32:16,080
Speaker 2: Really for my reports, like not emailing it to you.

699
00:32:17,880 --> 00:32:19,520
Speaker 3: It's amazing the stuff that I used to get in

700
00:32:19,600 --> 00:32:21,800
Speaker 3: the email And now I just go on the Internet

701
00:32:21,880 --> 00:32:22,040
Speaker 3: and I.

702
00:32:22,160 --> 00:32:22,840
Speaker 4: Just pull it right up.

703
00:32:22,880 --> 00:32:25,080
Speaker 3: So you just think about that transition. It's made my

704
00:32:25,200 --> 00:32:26,800
Speaker 3: job a lot easier. And I do feel like that

705
00:32:27,000 --> 00:32:29,000
Speaker 3: access to information is freeing.

706
00:32:29,240 --> 00:32:30,360
Speaker 4: How infladible that is.

707
00:32:30,440 --> 00:32:33,400
Speaker 3: That's something Elon talked about. He was talking up Starling,

708
00:32:33,520 --> 00:32:35,960
Speaker 3: but he said that's the great leveler in terms of

709
00:32:36,040 --> 00:32:38,960
Speaker 3: creating Internet access to everybody around the world. That means

710
00:32:39,080 --> 00:32:43,160
Speaker 3: everybody can get an education. M I tam cimate forces online.

711
00:32:42,800 --> 00:32:43,960
Speaker 4: Like you think about things like that.

712
00:32:44,240 --> 00:32:46,880
Speaker 3: But I feel like with this next Lettle level of AI,

713
00:32:46,960 --> 00:32:48,880
Speaker 3: I'm not quite so sure about it could be pretty cool.

714
00:32:49,480 --> 00:32:51,680
Speaker 2: Those are the good things that that's good, you know,

715
00:32:51,760 --> 00:32:53,360
Speaker 2: we think and that's what that was the promise of

716
00:32:53,440 --> 00:32:55,800
Speaker 2: the Internet. But look at the Internet's also been used

717
00:32:55,840 --> 00:32:57,280
Speaker 2: for a lot of really bad stuff. I mean, we

718
00:32:57,360 --> 00:33:00,400
Speaker 2: had the cover of Bloomberg business Week recently talk about

719
00:33:00,440 --> 00:33:04,960
Speaker 2: the way that social media platforms are becoming places where

720
00:33:05,240 --> 00:33:08,520
Speaker 2: teenage boys essentially have been held hostage and it's led

721
00:33:08,560 --> 00:33:10,959
Speaker 2: to some really discribbing outcomes. All right, again all as

722
00:33:11,000 --> 00:33:11,800
Speaker 2: a result of the Internet.

723
00:33:11,880 --> 00:33:14,240
Speaker 3: All right, So on two sides to every coin. And

724
00:33:14,320 --> 00:33:16,440
Speaker 3: this is what we want to get into because I'm

725
00:33:16,480 --> 00:33:19,000
Speaker 3: here at the Bloodberg Technology Summit. There's a workshop going on.

726
00:33:19,080 --> 00:33:21,560
Speaker 3: It's called addressing AI Risks in Business, and we have

727
00:33:21,640 --> 00:33:22,560
Speaker 3: a great voice to.

728
00:33:22,600 --> 00:33:23,360
Speaker 4: Talk to us about that.

729
00:33:23,640 --> 00:33:24,080
Speaker 5: Anthony A.

730
00:33:24,160 --> 00:33:26,280
Speaker 4: Giri is executive director of the Future of.

731
00:33:26,360 --> 00:33:29,120
Speaker 3: Life Institute, joining us on site. Thank you so much

732
00:33:29,280 --> 00:33:31,720
Speaker 3: for coming by with us. We are trying to figure

733
00:33:31,760 --> 00:33:35,840
Speaker 3: out technology AI, what it means for our workplace, for

734
00:33:36,000 --> 00:33:37,120
Speaker 3: our life in general.

735
00:33:37,480 --> 00:33:40,080
Speaker 4: What are some of the biggest the biggest facets of

736
00:33:40,120 --> 00:33:44,040
Speaker 4: that discussion or narrative that kind of occupy your mind today.

737
00:33:45,240 --> 00:33:48,040
Speaker 12: I think there are some major challenges that we have

738
00:33:48,680 --> 00:33:51,400
Speaker 12: that AI is presenting us with. One is the issue

739
00:33:51,480 --> 00:33:54,160
Speaker 12: of how are we going to make AI in a

740
00:33:54,360 --> 00:33:57,760
Speaker 12: way that is actually safe and beneficial for society. Right now,

741
00:33:57,960 --> 00:34:01,160
Speaker 12: we have AI systems that are being developed by sort

742
00:34:01,160 --> 00:34:04,160
Speaker 12: of a handful of very large companies, and they're doing

743
00:34:04,160 --> 00:34:06,760
Speaker 12: it in a very competitive way. So each of those

744
00:34:06,840 --> 00:34:09,919
Speaker 12: companies feels like it has to develop the new, better

745
00:34:10,040 --> 00:34:12,600
Speaker 12: system faster than its rivals. Does we even see this

746
00:34:12,680 --> 00:34:15,120
Speaker 12: on the international stage where that you know, the US

747
00:34:15,200 --> 00:34:17,680
Speaker 12: has rhetoric like we have to develop AI faster than

748
00:34:17,719 --> 00:34:18,880
Speaker 12: our geopolitical rivals.

749
00:34:19,360 --> 00:34:20,600
Speaker 9: And this is not the way.

750
00:34:21,080 --> 00:34:23,600
Speaker 12: It's a race, and a race is not the way

751
00:34:23,680 --> 00:34:25,840
Speaker 12: you get something to go as safely as possible.

752
00:34:25,960 --> 00:34:26,120
Speaker 3: Right.

753
00:34:26,560 --> 00:34:28,000
Speaker 12: The race is the way that you get things to

754
00:34:28,040 --> 00:34:30,800
Speaker 12: go as quickly as possible, and speed does not necessarily

755
00:34:30,840 --> 00:34:33,279
Speaker 12: equal safety. So I think one of the problems that

756
00:34:33,920 --> 00:34:35,840
Speaker 12: you know, we have to step out of this dynamic

757
00:34:36,040 --> 00:34:40,239
Speaker 12: of there's a competitive race to develop more and more

758
00:34:40,320 --> 00:34:43,320
Speaker 12: powerful systems, even at the expense of making sure that

759
00:34:43,400 --> 00:34:46,360
Speaker 12: those systems are safe and beneficial systems for society.

760
00:34:46,800 --> 00:34:47,640
Speaker 9: So that's issue one.

761
00:34:47,719 --> 00:34:50,399
Speaker 2: I say, what's at stake here? If those guardrails aren't

762
00:34:50,440 --> 00:34:53,880
Speaker 2: in place? I think, like, what's worst case scenario? The

763
00:34:54,440 --> 00:34:55,399
Speaker 2: get give it to us full.

764
00:34:56,520 --> 00:34:59,200
Speaker 12: I think they're short term scenarios that we see that

765
00:34:59,239 --> 00:35:01,600
Speaker 12: are that are worries and long term one. So I

766
00:35:01,640 --> 00:35:03,959
Speaker 12: think in the short term, we see that our whole

767
00:35:04,280 --> 00:35:09,680
Speaker 12: sort of information gathering and understanding and dissemination system are

768
00:35:09,880 --> 00:35:13,400
Speaker 12: the ecosphere is more or less being dismantled. You know,

769
00:35:13,800 --> 00:35:17,720
Speaker 12: right now we are becoming a wash in AI generated content.

770
00:35:18,120 --> 00:35:20,239
Speaker 12: You don't really know most of the things that you read,

771
00:35:20,280 --> 00:35:22,280
Speaker 12: whether they AI generated or human generated.

772
00:35:23,360 --> 00:35:23,400
Speaker 6: It.

773
00:35:23,960 --> 00:35:26,240
Speaker 12: We will soon be at the point where the pictures

774
00:35:26,320 --> 00:35:29,359
Speaker 12: that you see are more or less indistributable between AI

775
00:35:29,480 --> 00:35:33,560
Speaker 12: generated and camera generated. That's already sort of possible. So

776
00:35:33,680 --> 00:35:37,320
Speaker 12: we have a kind of pollution of our information commons

777
00:35:39,040 --> 00:35:41,120
Speaker 12: to the extent that I think we're losing track of

778
00:35:41,280 --> 00:35:44,600
Speaker 12: just what is real, what is human, what is true

779
00:35:45,280 --> 00:35:47,600
Speaker 12: from what isn't. And so I think that's a challenge

780
00:35:47,600 --> 00:35:49,480
Speaker 12: we're going to see play out in huge that's happening.

781
00:35:49,880 --> 00:35:50,560
Speaker 9: That's happening now.

782
00:35:51,120 --> 00:35:53,960
Speaker 2: I think that's not the robots taking over, that's that's.

783
00:35:53,920 --> 00:35:56,399
Speaker 12: Right it's not the robots taking over, but it also isn't.

784
00:35:57,120 --> 00:35:59,560
Speaker 12: It's also not not the robots taking over in the

785
00:35:59,640 --> 00:36:03,520
Speaker 12: sense that we are replacing human generated culture and content

786
00:36:03,760 --> 00:36:05,440
Speaker 12: with AI generated culture and content.

787
00:36:06,239 --> 00:36:09,360
Speaker 4: How do you police that? How do you create the

788
00:36:09,480 --> 00:36:10,840
Speaker 4: guardrails around that?

789
00:36:12,040 --> 00:36:14,600
Speaker 12: I think there it's a So in terms of the

790
00:36:14,960 --> 00:36:18,920
Speaker 12: content generation, it's a question of having ways to verify

791
00:36:19,080 --> 00:36:21,600
Speaker 12: that something is authentic, that is real, that is true,

792
00:36:21,680 --> 00:36:24,680
Speaker 12: that is human generated ways to detect and ways to

793
00:36:24,800 --> 00:36:28,360
Speaker 12: label things that aren't, and having the technical systems that

794
00:36:28,480 --> 00:36:32,000
Speaker 12: allow people to access that information. So it should be

795
00:36:32,200 --> 00:36:34,040
Speaker 12: the case that if I go online and read a

796
00:36:34,080 --> 00:36:37,080
Speaker 12: news article, I should know whether it's AI written or not.

797
00:36:37,600 --> 00:36:40,080
Speaker 12: I should know whether a fact and it is true,

798
00:36:40,160 --> 00:36:42,120
Speaker 12: and if it's claimed to be true, how do I.

799
00:36:42,160 --> 00:36:43,400
Speaker 9: Know that it's true? I should be able to go

800
00:36:43,560 --> 00:36:45,440
Speaker 9: back to like the camera they took that picture?

801
00:36:45,520 --> 00:36:46,239
Speaker 3: How do you do that?

802
00:36:46,600 --> 00:36:49,960
Speaker 12: Well, it's all possible technically, I mean the social systems

803
00:36:50,040 --> 00:36:53,360
Speaker 12: and the like, and the the rules and the market

804
00:36:53,440 --> 00:36:55,640
Speaker 12: demand to bring those things into the way we.

805
00:36:55,640 --> 00:37:01,080
Speaker 2: Actually need use Berg as this article was written in

806
00:37:01,120 --> 00:37:05,239
Speaker 2: the system of Bloomberg automation. I mean, not everybody does that, though,

807
00:37:05,280 --> 00:37:06,279
Speaker 2: I guess right.

808
00:37:06,440 --> 00:37:10,040
Speaker 12: So, so should there be a rule a law, of course,

809
00:37:10,640 --> 00:37:14,960
Speaker 12: I mean there should certainly be certain rules about fake information,

810
00:37:15,160 --> 00:37:17,160
Speaker 12: so it should. I think it should not be legal

811
00:37:17,280 --> 00:37:21,200
Speaker 12: to generate non consensual deep fakes of people that they

812
00:37:21,280 --> 00:37:24,960
Speaker 12: didn't authorize, whether they're sexual deep fakes or political deep

813
00:37:24,960 --> 00:37:28,239
Speaker 12: fakes that are or fraudulent business you know deep talking

814
00:37:28,320 --> 00:37:29,280
Speaker 12: to politicians.

815
00:37:28,920 --> 00:37:31,560
Speaker 2: Right now to make this reality we are.

816
00:37:32,080 --> 00:37:32,319
Speaker 6: Yeah.

817
00:37:32,560 --> 00:37:36,080
Speaker 12: So there are lots of bills in Congress that are

818
00:37:36,160 --> 00:37:39,440
Speaker 12: pursuing the deep fakes issue. I think some of them

819
00:37:40,040 --> 00:37:44,160
Speaker 12: are underpowered in the sense that they predominantly put the

820
00:37:44,800 --> 00:37:48,399
Speaker 12: put the onus on content creators not to create deep fakes,

821
00:37:48,440 --> 00:37:50,719
Speaker 12: but they don't put the onus on developers not to

822
00:37:50,840 --> 00:37:55,000
Speaker 12: create the tools for deep fakes, and not necessarily enough

823
00:37:55,040 --> 00:37:58,000
Speaker 12: on platforms to make sure that they label things correctly.

824
00:37:58,239 --> 00:38:00,279
Speaker 9: So I think we need lots of things there.

825
00:38:00,560 --> 00:38:02,799
Speaker 12: Some of those will happen by themselves because the market

826
00:38:02,840 --> 00:38:04,840
Speaker 12: will require some of them will be required.

827
00:38:05,120 --> 00:38:06,959
Speaker 9: We will need regulations in law scot I'm.

828
00:38:06,880 --> 00:38:09,040
Speaker 3: Just kind of a little terrified thinking about social media.

829
00:38:09,080 --> 00:38:11,560
Speaker 3: I the amount of news information that's not accurate that's

830
00:38:11,560 --> 00:38:13,560
Speaker 3: already out there, and we've not done a great job

831
00:38:14,880 --> 00:38:15,520
Speaker 3: policing that.

832
00:38:15,760 --> 00:38:19,040
Speaker 4: So I'm my favors crossing everything.

833
00:38:19,160 --> 00:38:21,320
Speaker 9: You need to do a little better this time, you

834
00:38:21,440 --> 00:38:22,080
Speaker 9: need to do a little better.

835
00:38:22,120 --> 00:38:22,680
Speaker 4: We've learned.

836
00:38:22,719 --> 00:38:24,120
Speaker 2: We'll see. We'll check in with you in a little

837
00:38:24,120 --> 00:38:25,560
Speaker 2: while and see if we've done a better job.

838
00:38:25,960 --> 00:38:28,239
Speaker 9: But the broader question, so you asked about the long

839
00:38:28,320 --> 00:38:29,480
Speaker 9: term and the short.

840
00:38:29,320 --> 00:38:32,279
Speaker 2: Term, so we'll see, Well, we'll see if it We'll

841
00:38:32,280 --> 00:38:33,759
Speaker 2: see if we can if we can pull it off.

842
00:38:34,040 --> 00:38:36,160
Speaker 2: All right, Anthony, thank you so much for joining us.

843
00:38:36,200 --> 00:38:38,839
Speaker 2: That's Anthony Gary, executive director of the Future of Light

844
00:38:38,960 --> 00:38:41,080
Speaker 2: and Light Institute, here at the Bloomberg Tech Summit.

845
00:38:43,280 --> 00:38:46,759
Speaker 1: You're listening to the Bloomberg Business Week podcast. Catch us

846
00:38:46,840 --> 00:38:50,040
Speaker 1: Live weekday afternoons from two to five pm Eastern Listen

847
00:38:50,120 --> 00:38:52,279
Speaker 1: on Apple card Play and then brought auto with a

848
00:38:52,320 --> 00:38:55,320
Speaker 1: Bloomberg Business at or want us live on YouTube.

849
00:38:57,520 --> 00:38:59,680
Speaker 2: Well, our next guest needs going to We're going to

850
00:38:59,719 --> 00:39:02,480
Speaker 2: get them one anyway. It's a Silicon value vis He

851
00:39:02,560 --> 00:39:05,680
Speaker 2: helped build PayPal, co fided LinkedIn. He was an early

852
00:39:05,760 --> 00:39:08,960
Speaker 2: investor in Facebook and Airbnb. He was the first investor

853
00:39:09,000 --> 00:39:11,680
Speaker 2: in open Ai. He co founded the ai company in

854
00:39:11,800 --> 00:39:14,880
Speaker 2: Flection Ai. He's on Microsoft Forard. He's a partner at

855
00:39:14,880 --> 00:39:17,399
Speaker 2: the VC firm Braylock Partners. He's a New York Times

856
00:39:17,480 --> 00:39:20,879
Speaker 2: bestselling author, and he's also a podcast we have read

857
00:39:20,920 --> 00:39:21,279
Speaker 2: hot mon.

858
00:39:22,040 --> 00:39:23,360
Speaker 7: Great to be here, Thank you for you.

859
00:39:24,960 --> 00:39:27,120
Speaker 10: As we were just talking how does the environment? But

860
00:39:27,239 --> 00:39:29,200
Speaker 10: we've ever done a radio show before it so hopefully

861
00:39:29,200 --> 00:39:29,560
Speaker 10: it'll work.

862
00:39:29,800 --> 00:39:31,560
Speaker 9: It's working so far, so far, so good.

863
00:39:31,560 --> 00:39:35,480
Speaker 2: But I have to ask you, are you real or

864
00:39:35,600 --> 00:39:38,319
Speaker 2: am I talking to some sort of AI digital twin

865
00:39:38,440 --> 00:39:41,479
Speaker 2: rand Well, and explain why I have to ask that question.

866
00:39:41,960 --> 00:39:45,240
Speaker 10: So as you as you know, I did an interview

867
00:39:45,280 --> 00:39:47,760
Speaker 10: with myself where I was talking to my own digital

868
00:39:47,840 --> 00:39:50,279
Speaker 10: twin read AI, and I did it in order to

869
00:39:50,400 --> 00:39:53,279
Speaker 10: kind of show that not only obviously there's a whole

870
00:39:53,280 --> 00:39:53,920
Speaker 10: bunch of concerns.

871
00:39:53,960 --> 00:39:55,520
Speaker 7: There's concerns and political.

872
00:39:55,200 --> 00:39:58,800
Speaker 10: And misinformation and a deep makes and a bunch of

873
00:39:58,840 --> 00:40:01,799
Speaker 10: other stuff that are real con but even here there's

874
00:40:02,040 --> 00:40:04,480
Speaker 10: optimism to look at what's to be shaped towards. And

875
00:40:04,560 --> 00:40:08,400
Speaker 10: I wanted to show not just hell, that message. And

876
00:40:08,480 --> 00:40:10,160
Speaker 10: so I was like, okay, well let me talk to

877
00:40:10,239 --> 00:40:13,120
Speaker 10: my own three day I and see how it works.

878
00:40:13,640 --> 00:40:15,399
Speaker 10: And so and the reason why you asked that question

879
00:40:15,440 --> 00:40:16,880
Speaker 10: of course, because you said, well, wait a minute, that

880
00:40:17,000 --> 00:40:17,600
Speaker 10: was pretty good.

881
00:40:18,040 --> 00:40:20,920
Speaker 7: It was we're here in person. You can tell this

882
00:40:21,120 --> 00:40:22,040
Speaker 7: is I'm the real name.

883
00:40:22,600 --> 00:40:24,920
Speaker 2: That sounds like this looked like you, Yes, yeah, this

884
00:40:25,000 --> 00:40:27,040
Speaker 2: is like it looked like talking.

885
00:40:26,840 --> 00:40:31,240
Speaker 7: To a video audio and the words we're all AI

886
00:40:31,480 --> 00:40:37,239
Speaker 7: generated zero from me. It was other than mirroring off

887
00:40:37,840 --> 00:40:39,239
Speaker 7: my earlier videos.

888
00:40:38,960 --> 00:40:42,160
Speaker 2: Mirroring off my earlier podcast, use your your use your

889
00:40:42,760 --> 00:40:43,799
Speaker 2: book to train as well.

890
00:40:43,960 --> 00:40:48,120
Speaker 10: Yes, okay, yeah, it used all everything ever did answers

891
00:40:48,400 --> 00:40:50,360
Speaker 10: the way read would get answers.

892
00:40:50,800 --> 00:40:53,680
Speaker 3: We're all gonna have digital twins someday and we're gonna

893
00:40:53,680 --> 00:40:56,000
Speaker 3: hear it was and we're like, go.

894
00:40:56,000 --> 00:40:56,840
Speaker 2: To the d m V for me.

895
00:40:59,320 --> 00:41:03,200
Speaker 7: So I think we must. It's certainly possible.

896
00:41:03,680 --> 00:41:07,680
Speaker 10: Certainly, if you're a media person you'll have a digital twin. Certainly,

897
00:41:07,760 --> 00:41:10,200
Speaker 10: if you're a leader, you'll have a digital twin.

898
00:41:10,320 --> 00:41:11,319
Speaker 7: It'll be used in a front.

899
00:41:11,360 --> 00:41:13,319
Speaker 10: If you're a lawyer, you'll have one because then people

900
00:41:13,360 --> 00:41:15,719
Speaker 10: can talk to your digital twins and maybe at a

901
00:41:15,800 --> 00:41:16,239
Speaker 10: cheaper rate.

902
00:41:16,280 --> 00:41:18,400
Speaker 7: The dog you are prepping for your meeting or that

903
00:41:18,480 --> 00:41:18,919
Speaker 7: kind of thing.

904
00:41:19,040 --> 00:41:20,920
Speaker 10: So I think it will be a stack of harriers

905
00:41:21,000 --> 00:41:23,160
Speaker 10: for digital twins will be very real.

906
00:41:24,040 --> 00:41:26,400
Speaker 7: But I'm not sure all eight billion people in the

907
00:41:26,440 --> 00:41:27,000
Speaker 7: world will.

908
00:41:26,880 --> 00:41:30,160
Speaker 2: This string well, so why should this excite us and

909
00:41:30,239 --> 00:41:32,480
Speaker 2: not scare us and want us to all move to bunkers.

910
00:41:33,640 --> 00:41:37,880
Speaker 10: So it should look naturally, it should both excite and

911
00:41:38,160 --> 00:41:41,000
Speaker 10: scare us. The scars is, the negatives is the scarris

912
00:41:41,120 --> 00:41:44,839
Speaker 10: is of political misinformation. You know, things that are going

913
00:41:44,880 --> 00:41:47,080
Speaker 10: to be happening this year in the US until we're

914
00:41:47,520 --> 00:41:51,480
Speaker 10: in other major elections of democracy. It should scare us

915
00:41:51,520 --> 00:41:54,919
Speaker 10: because there's questions around them. And let if someone created

916
00:41:55,000 --> 00:41:59,040
Speaker 10: a deep make of me doing something or saying something

917
00:41:59,120 --> 00:42:01,880
Speaker 10: I really am like you know, like you know that

918
00:42:02,200 --> 00:42:04,920
Speaker 10: I think Trump is a corruption in democracy against the

919
00:42:04,960 --> 00:42:08,600
Speaker 10: rule on what happens to you. I love Trump, you know,

920
00:42:08,880 --> 00:42:11,200
Speaker 10: that would be obviously a problem. And so there's all

921
00:42:11,400 --> 00:42:14,200
Speaker 10: kinds of ways that there are real problems. So on

922
00:42:14,280 --> 00:42:17,960
Speaker 10: the other hand, we will we will, as we always

923
00:42:18,000 --> 00:42:20,440
Speaker 10: do with technology to figure out how to navigation, will

924
00:42:20,440 --> 00:42:24,920
Speaker 10: get that not perfectly, but sufficiently, and there will be

925
00:42:25,000 --> 00:42:28,120
Speaker 10: great things like for example, that's part of the reason

926
00:42:28,200 --> 00:42:29,680
Speaker 10: I started showing, Hey.

927
00:42:29,600 --> 00:42:33,120
Speaker 7: Look here, I'm making a deep bag of myself and

928
00:42:34,160 --> 00:42:34,879
Speaker 7: it's not that bad.

929
00:42:36,160 --> 00:42:39,160
Speaker 4: You know, read when you sat down I said, the conversations.

930
00:42:39,200 --> 00:42:41,080
Speaker 3: We have a lot of Bloemberg and we've written about

931
00:42:41,080 --> 00:42:41,960
Speaker 3: this is here.

932
00:42:42,080 --> 00:42:45,440
Speaker 4: You have Lincoln kind of the social media site, that

933
00:42:45,680 --> 00:42:47,360
Speaker 4: kind of the social media site.

934
00:42:47,120 --> 00:42:50,080
Speaker 3: That seems to be clean, pure, You can truss and

935
00:42:50,160 --> 00:42:53,640
Speaker 3: you can have conversations, so we can get it right.

936
00:42:54,200 --> 00:42:56,400
Speaker 4: So what how is that kind of a guide in

937
00:42:56,480 --> 00:42:58,960
Speaker 4: yours since in terms of jen ai, and how do

938
00:42:59,080 --> 00:43:01,600
Speaker 4: you think about and what are the guard rails? How

939
00:43:01,640 --> 00:43:02,520
Speaker 4: do we regulate it?

940
00:43:02,760 --> 00:43:06,400
Speaker 10: Yes, and so the good news is the mostly frontier

941
00:43:06,480 --> 00:43:11,480
Speaker 10: model companies, Microsoft, open Ai, Google are all putting a

942
00:43:11,600 --> 00:43:12,800
Speaker 10: lot of energy in that.

943
00:43:13,200 --> 00:43:16,560
Speaker 7: They're making these models healthy.

944
00:43:16,360 --> 00:43:21,160
Speaker 10: Participants within the US, within the global kind of media ecosystem,

945
00:43:21,280 --> 00:43:24,600
Speaker 10: like don't generate fake information, be.

946
00:43:24,800 --> 00:43:28,920
Speaker 7: Civil, you don't do hate speech, don't enab it self harm.

947
00:43:29,320 --> 00:43:31,200
Speaker 7: They all put a lot of energy and a lot

948
00:43:31,280 --> 00:43:34,200
Speaker 7: of work in that. And I don't invest hundreds of

949
00:43:34,320 --> 00:43:37,239
Speaker 7: millions of dollars and teams of hundreds of people in

950
00:43:37,360 --> 00:43:37,800
Speaker 7: order to do that.

951
00:43:38,640 --> 00:43:41,440
Speaker 10: Now, one of the challenges is is that some of

952
00:43:41,520 --> 00:43:44,360
Speaker 10: these models are open source, and as an open source,

953
00:43:44,640 --> 00:43:46,920
Speaker 10: someone else takes the open source model to.

954
00:43:47,000 --> 00:43:49,000
Speaker 7: Something that's not any of that. With that right and

955
00:43:49,080 --> 00:43:50,440
Speaker 7: that's part of the reason why we still have to

956
00:43:50,560 --> 00:43:51,120
Speaker 7: navigate this.

957
00:43:51,560 --> 00:43:53,400
Speaker 3: Do you have trust so that we can navigate it

958
00:43:53,480 --> 00:43:57,000
Speaker 3: correctly and safely in the future AI, So.

959
00:43:57,120 --> 00:43:59,319
Speaker 7: The ultimate answer is absolutely yes.

960
00:44:00,440 --> 00:44:05,200
Speaker 10: That doesn't mean that there aren't potholes, you know, bumps

961
00:44:05,239 --> 00:44:08,120
Speaker 10: in the road, thunder scrapes, all the rest, and it's

962
00:44:08,600 --> 00:44:10,440
Speaker 10: I guarantee you there will be those two.

963
00:44:11,400 --> 00:44:14,400
Speaker 2: The US can't get Vladimir Putin not to invade in Ukraine.

964
00:44:14,719 --> 00:44:17,600
Speaker 2: The world can't get him not sure stop fighting. What

965
00:44:17,800 --> 00:44:21,560
Speaker 2: convinces you that we could get a leader like him

966
00:44:21,960 --> 00:44:22,800
Speaker 2: not to use AI?

967
00:44:23,400 --> 00:44:25,000
Speaker 7: Oh bad? Zero percent?

968
00:44:25,640 --> 00:44:30,920
Speaker 10: Like if you said that that Putin will be using

969
00:44:32,080 --> 00:44:37,120
Speaker 10: AI to disrupt the US elections, you know, probably support

970
00:44:37,200 --> 00:44:41,279
Speaker 10: Trump because that's his exit strategy from Ukraine. Uh, he

971
00:44:41,400 --> 00:44:43,520
Speaker 10: will do that one hundred percent. Is no way it's

972
00:44:43,520 --> 00:44:46,200
Speaker 10: having it. The defense is not getting him not to

973
00:44:46,280 --> 00:44:49,560
Speaker 10: do it. The defense is integrating AI in our own

974
00:44:49,640 --> 00:44:53,440
Speaker 10: tech platforms and using our stronger AI in defense.

975
00:44:53,760 --> 00:44:56,680
Speaker 2: So essentially saying, okay, well, maybe you see something on

976
00:44:56,960 --> 00:45:00,800
Speaker 2: Facebook and that platforms might have developed something that's okay,

977
00:45:00,920 --> 00:45:05,520
Speaker 2: Well this isn't necessarily something that's true, Yes, but do

978
00:45:05,560 --> 00:45:06,920
Speaker 2: you trust Do you trust Facebook?

979
00:45:06,960 --> 00:45:07,239
Speaker 12: To do that.

980
00:45:08,080 --> 00:45:09,200
Speaker 7: Actually, I.

981
00:45:11,480 --> 00:45:13,640
Speaker 2: Yes, And you have a deep history with the company.

982
00:45:13,800 --> 00:45:15,160
Speaker 7: Yes, I have a deep history of the company.

983
00:45:15,400 --> 00:45:17,520
Speaker 10: I actually have talked to Zuckerbiger about it, and they're

984
00:45:17,560 --> 00:45:20,120
Speaker 10: investing in it. I think they're making a real effort

985
00:45:20,120 --> 00:45:23,839
Speaker 10: at it, so I think like they're really trying. I'm

986
00:45:23,880 --> 00:45:27,040
Speaker 10: more worried about Twitter that I am about Facebook on

987
00:45:27,200 --> 00:45:27,600
Speaker 10: this matter.

988
00:45:28,040 --> 00:45:29,520
Speaker 2: Okay, why are you more worried about Twitter?

989
00:45:29,600 --> 00:45:33,560
Speaker 10: Well, because look someone you also go way yeah, and look,

990
00:45:33,640 --> 00:45:36,400
Speaker 10: Elon is A is literally one of the heroes of

991
00:45:36,480 --> 00:45:41,000
Speaker 10: our generation. You know, uh, Space and I think and

992
00:45:41,239 --> 00:45:44,200
Speaker 10: kind of Starlink and Tesla and Evie.

993
00:45:44,120 --> 00:45:46,600
Speaker 7: Just amazing, amazing, amazing. We wouldn't have any of this

994
00:45:46,680 --> 00:45:48,799
Speaker 7: about him. It is so spectacular.

995
00:45:49,400 --> 00:45:52,080
Speaker 10: On the other hand, before he buys Twitter, he's like, oh,

996
00:45:52,239 --> 00:45:55,279
Speaker 10: we have this problem with robots. And then after he

997
00:45:55,400 --> 00:45:57,239
Speaker 10: bought trying to get out of the and then after

998
00:45:57,280 --> 00:46:00,960
Speaker 10: you buy what robots and you're like, well, the robots

999
00:46:00,960 --> 00:46:04,040
Speaker 10: are still there. There's still like Internet Research Agency watching

1000
00:46:04,160 --> 00:46:05,560
Speaker 10: robot farms doing shit.

1001
00:46:05,800 --> 00:46:07,640
Speaker 7: Are you doing anything about them?

1002
00:46:07,680 --> 00:46:10,879
Speaker 2: I'm not hearing anything, to be fair, I did see

1003
00:46:11,080 --> 00:46:13,920
Speaker 2: I did see an ad on Twitter recently with a

1004
00:46:14,000 --> 00:46:17,120
Speaker 2: deep bake of Jeff Bezos pushing a cryptocurrency. Yeah, it's

1005
00:46:17,120 --> 00:46:19,600
Speaker 2: a paid ad. Yes that has showed up repeatedly in

1006
00:46:19,680 --> 00:46:21,319
Speaker 2: my feed obviously not approved vice.

1007
00:46:21,719 --> 00:46:25,040
Speaker 7: Yes, yes, yeah, and so therefore, what are you doing

1008
00:46:25,160 --> 00:46:26,200
Speaker 7: about AI and JEMA?

1009
00:46:26,400 --> 00:46:28,360
Speaker 4: Well, you know, Elon is so complex.

1010
00:46:29,000 --> 00:46:31,480
Speaker 3: Back from Milton, he did a conversation with Michael Milton.

1011
00:46:31,560 --> 00:46:34,880
Speaker 3: He said, very important to have maximum seeking AI and

1012
00:46:35,040 --> 00:46:36,359
Speaker 3: maximum curious.

1013
00:46:36,040 --> 00:46:38,759
Speaker 4: AI, and then it's tied not to lie or do

1014
00:46:38,920 --> 00:46:40,040
Speaker 4: things that are not true.

1015
00:46:40,840 --> 00:46:44,320
Speaker 7: Well, walk the lock, don't just talk the talk, well.

1016
00:46:44,200 --> 00:46:47,080
Speaker 3: Said, can I ask, I'm curious if we have all

1017
00:46:47,120 --> 00:46:49,759
Speaker 3: these conversations about jen Ai, how is it going to

1018
00:46:49,840 --> 00:46:51,880
Speaker 3: impact my life? How is it really going to impact

1019
00:46:51,960 --> 00:46:54,120
Speaker 3: him's life? How's it really going to impact your life?

1020
00:46:54,920 --> 00:46:58,320
Speaker 7: So here's a simple way of looking at all of

1021
00:46:58,440 --> 00:47:01,480
Speaker 7: us are going to have as an all AI assistant

1022
00:47:01,960 --> 00:47:04,319
Speaker 7: that it is going to be helping us with its

1023
00:47:04,360 --> 00:47:07,960
Speaker 7: focus on us. Right, so it'll be like, hey, what

1024
00:47:08,040 --> 00:47:09,480
Speaker 7: do you need help with? Do you need help with

1025
00:47:10,080 --> 00:47:12,879
Speaker 7: figuring out say something like like where to go? What's

1026
00:47:13,000 --> 00:47:15,480
Speaker 7: entertaining to do? You know in the city?

1027
00:47:15,640 --> 00:47:19,040
Speaker 10: And I or oh I'm having this different I'm trying

1028
00:47:19,040 --> 00:47:20,880
Speaker 10: to figure out this thing with my kids. Oh, we

1029
00:47:20,960 --> 00:47:22,799
Speaker 10: can help you with that. Oh, I'm trying to figure

1030
00:47:22,880 --> 00:47:24,960
Speaker 10: this medical thing. Oh I can help you with that. Oh,

1031
00:47:25,000 --> 00:47:27,160
Speaker 10: I'm trying to figure out I had this difficult conversation

1032
00:47:27,239 --> 00:47:27,920
Speaker 10: with coworker.

1033
00:47:28,200 --> 00:47:30,520
Speaker 7: Oh, I'll talk to you about that. It will help

1034
00:47:30,600 --> 00:47:32,720
Speaker 7: us with a whole range of things. And like, for example,

1035
00:47:32,800 --> 00:47:33,920
Speaker 7: part of what we found.

1036
00:47:33,719 --> 00:47:36,720
Speaker 10: An inflection was really surprising to us and very positive.

1037
00:47:37,280 --> 00:47:41,840
Speaker 10: Which has podge in personal intelligence is people.

1038
00:47:41,600 --> 00:47:43,839
Speaker 7: Will say, oh, I got these eight ingredients in my friends,

1039
00:47:43,880 --> 00:47:44,480
Speaker 7: what should I name?

1040
00:47:44,920 --> 00:47:47,520
Speaker 10: Or oh, my toaster broke and I fixed it, and

1041
00:47:47,640 --> 00:47:50,239
Speaker 10: you're like, actually, in fact, it helps all of that.

1042
00:47:50,440 --> 00:47:53,960
Speaker 10: So that's the exact human amplification that helping us navigate

1043
00:47:54,000 --> 00:47:58,120
Speaker 10: our lives, which is the thing I guarantee will be there.

1044
00:47:58,280 --> 00:48:00,680
Speaker 2: I love the ingredients in the fridge of it. If

1045
00:48:00,680 --> 00:48:02,440
Speaker 2: you're just joining us, we're speaking to with Rida Hoffman,

1046
00:48:02,600 --> 00:48:07,640
Speaker 2: the PayPal of LinkedIn, Open Ai, of Inflection AI, of

1047
00:48:07,760 --> 00:48:13,279
Speaker 2: the podcast Possible. But this certainly goes on read I'm

1048
00:48:13,360 --> 00:48:16,439
Speaker 2: wondering about winners and losers when it comes to AI AI.

1049
00:48:16,520 --> 00:48:19,440
Speaker 2: In terms of companies. You're on the board of Microsoft Imagined,

1050
00:48:19,440 --> 00:48:21,520
Speaker 2: A couple of the big companies that you've invested in,

1051
00:48:22,160 --> 00:48:26,480
Speaker 2: that you've founded. Is there a concern that we're just

1052
00:48:26,560 --> 00:48:30,120
Speaker 2: going to see the big companies like Microsoft, like in video,

1053
00:48:31,000 --> 00:48:33,840
Speaker 2: like Amazon, like meta platforms, they're going to be the

1054
00:48:33,920 --> 00:48:35,359
Speaker 2: only winners when it comes to AI.

1055
00:48:36,000 --> 00:48:37,680
Speaker 7: What I guarantee you They're not going to be the

1056
00:48:37,760 --> 00:48:39,440
Speaker 7: only one. They will be winners.

1057
00:48:39,800 --> 00:48:39,960
Speaker 6: Right.

1058
00:48:41,360 --> 00:48:45,560
Speaker 10: Satia has just done a masterful job, like like remember

1059
00:48:45,680 --> 00:48:49,040
Speaker 10: it started with a million dollars deal with a five

1060
00:48:49,080 --> 00:48:51,120
Speaker 10: O WEBC three. I think in the first time in

1061
00:48:51,160 --> 00:48:55,080
Speaker 10: the history something like that's happening, like genius strategy from

1062
00:48:55,160 --> 00:48:58,000
Speaker 10: talking and I think it will be Uh, it'll across

1063
00:48:58,080 --> 00:49:00,400
Speaker 10: all of these companies. I think they will have wrong

1064
00:49:00,480 --> 00:49:02,960
Speaker 10: winds going into the future. On the other hands, you know,

1065
00:49:03,000 --> 00:49:05,120
Speaker 10: at Gray Locke, we're investing a whole bunch of AI company.

1066
00:49:05,160 --> 00:49:06,279
Speaker 10: We do it reasonably well.

1067
00:49:06,400 --> 00:49:09,360
Speaker 7: We have a whole portfolio that we're excited about. And

1068
00:49:09,840 --> 00:49:13,320
Speaker 7: I think that the I think that it's just you

1069
00:49:13,400 --> 00:49:15,360
Speaker 7: don't play the same game. It's a little bit like

1070
00:49:15,400 --> 00:49:15,920
Speaker 7: and you said.

1071
00:49:15,800 --> 00:49:18,000
Speaker 10: Hey, I got a start up, I'm gonna try to

1072
00:49:18,040 --> 00:49:19,640
Speaker 10: make a new desktop search company.

1073
00:49:19,640 --> 00:49:22,360
Speaker 7: You're like, well, one of the difficults, right there is

1074
00:49:22,640 --> 00:49:23,680
Speaker 7: this company called Google.

1075
00:49:23,719 --> 00:49:27,600
Speaker 10: It's a little challenging. Okay, you know I'm gonna make

1076
00:49:27,640 --> 00:49:30,839
Speaker 10: a new handset mobile phone devices. You're like, oh, there's

1077
00:49:30,880 --> 00:49:32,320
Speaker 10: this company called that whole it's.

1078
00:49:32,160 --> 00:49:34,480
Speaker 7: A little challenging, so you know, you don't do it

1079
00:49:34,560 --> 00:49:36,560
Speaker 7: that way. But there's gonna be a whole range of

1080
00:49:36,640 --> 00:49:39,799
Speaker 7: other AI companies the things that will like.

1081
00:49:40,440 --> 00:49:43,439
Speaker 10: Everything from opportunities that the large tech companies just can't

1082
00:49:43,440 --> 00:49:43,920
Speaker 10: focus on.

1083
00:49:44,400 --> 00:49:47,359
Speaker 7: To taking interesting risks and making that risk look good

1084
00:49:47,440 --> 00:49:49,560
Speaker 7: and then all of a sudden building out ding. So

1085
00:49:49,560 --> 00:49:51,719
Speaker 7: I think there's gonna be a range of startups. Don't

1086
00:49:51,760 --> 00:49:54,000
Speaker 7: look excited the lights podcasts.

1087
00:49:54,239 --> 00:49:56,920
Speaker 4: We have a great podcast. You have a great new podcast.

1088
00:49:56,960 --> 00:50:00,800
Speaker 4: Tell us about possible, U tell us about it? Like,

1089
00:50:01,120 --> 00:50:02,640
Speaker 4: really want to talk too?

1090
00:50:03,200 --> 00:50:06,200
Speaker 7: What are the conversations you want to have? So for me,

1091
00:50:06,560 --> 00:50:07,240
Speaker 7: it's possible.

1092
00:50:08,040 --> 00:50:11,480
Speaker 10: It's too much of the dialogue is about technology being

1093
00:50:11,600 --> 00:50:15,440
Speaker 10: dangerous to us, how we evolve this.

1094
00:50:15,520 --> 00:50:19,320
Speaker 7: Humanity, how we become better and more human. It's food, technology,

1095
00:50:19,880 --> 00:50:21,720
Speaker 7: it's it's clothing, it's.

1096
00:50:21,719 --> 00:50:25,440
Speaker 10: Glasses, it's it's my it's food, it's cooking, it's fired,

1097
00:50:25,480 --> 00:50:26,160
Speaker 10: it's buildings.

1098
00:50:26,480 --> 00:50:27,000
Speaker 7: All of this is.

1099
00:50:27,080 --> 00:50:30,120
Speaker 10: Technology that's helped us make us as the human games

1100
00:50:30,160 --> 00:50:32,719
Speaker 10: we are AI will be saying, because.

1101
00:50:32,480 --> 00:50:35,759
Speaker 7: The whole point I'm on the Possible podcast used to say,

1102
00:50:36,680 --> 00:50:38,960
Speaker 7: this is how technology can help.

1103
00:50:38,840 --> 00:50:40,879
Speaker 10: Us become more human, not to go, oh my god,

1104
00:50:41,040 --> 00:50:44,600
Speaker 10: what's coming in technology. It's it can be great for us,

1105
00:50:44,840 --> 00:50:47,359
Speaker 10: and it's not that hard to shape. We just need

1106
00:50:47,440 --> 00:50:50,640
Speaker 10: to shape it, whether it's AI, whether it's the use

1107
00:50:50,719 --> 00:50:53,080
Speaker 10: of phones. And you know, obviously people like to talk

1108
00:50:53,080 --> 00:50:55,480
Speaker 10: about what social networks and they go, wow, that's been

1109
00:50:55,520 --> 00:50:57,200
Speaker 10: to have a challenging It's like, well, look at LinkedIn.

1110
00:50:57,680 --> 00:51:00,600
Speaker 10: LinkedIn's worked out pretty well. It's doable. It's doable, so

1111
00:51:00,719 --> 00:51:01,200
Speaker 10: let's do it.

1112
00:51:01,600 --> 00:51:03,319
Speaker 2: We don't want to end with something that everybody loves

1113
00:51:03,360 --> 00:51:04,800
Speaker 2: to talk about. Politics.

1114
00:51:06,480 --> 00:51:07,320
Speaker 4: We all agree.

1115
00:51:09,120 --> 00:51:11,440
Speaker 2: Look, you've been open about your feelings about Dwain Trump.

1116
00:51:11,560 --> 00:51:14,360
Speaker 2: You help fund e Gene Carroll's defamation lawsuits against the

1117
00:51:14,440 --> 00:51:18,440
Speaker 2: former president. There are a lot of business leaders out there,

1118
00:51:18,840 --> 00:51:21,320
Speaker 2: some of them people you work with very closely, to

1119
00:51:21,440 --> 00:51:25,359
Speaker 2: say that business under a President Trump administration was better

1120
00:51:25,440 --> 00:51:29,200
Speaker 2: than business under a Biden administration. And that's why we're

1121
00:51:29,239 --> 00:51:31,759
Speaker 2: in the corner of Donald Trump. When it comes to

1122
00:51:31,840 --> 00:51:33,719
Speaker 2: twenty twenty four, What do you say to them?

1123
00:51:34,200 --> 00:51:38,160
Speaker 10: So I understand the approach that Trump reason is more

1124
00:51:38,280 --> 00:51:41,440
Speaker 10: regulatory lightweight than Biden. And by the way, generally speaking,

1125
00:51:41,520 --> 00:51:44,359
Speaker 10: being more regulatory lightweight is a good thing. I try

1126
00:51:44,440 --> 00:51:46,960
Speaker 10: to encourage the Biden administration to do that. On the

1127
00:51:47,040 --> 00:51:49,560
Speaker 10: other hand, what's more fundamental is the rule of law.

1128
00:51:50,160 --> 00:51:53,919
Speaker 10: Business works much much better with a healthy, strong rule

1129
00:51:53,960 --> 00:51:54,239
Speaker 10: of law.

1130
00:51:54,800 --> 00:51:57,040
Speaker 7: We do not want to have leaders who are literally

1131
00:51:57,960 --> 00:51:59,200
Speaker 7: in a court like have.

1132
00:51:59,320 --> 00:52:02,759
Speaker 10: Been convicted by court of you were slandering about your

1133
00:52:02,880 --> 00:52:07,239
Speaker 10: sexual assault and a jury found you guilty. Right, we

1134
00:52:07,400 --> 00:52:10,600
Speaker 10: should be a rule of law country first. And by

1135
00:52:10,640 --> 00:52:12,239
Speaker 10: the way, this is what's the business leaders. By the way,

1136
00:52:12,360 --> 00:52:14,280
Speaker 10: rule of law is what makes great business.

1137
00:52:14,719 --> 00:52:15,520
Speaker 7: That's the reason why.

1138
00:52:15,600 --> 00:52:18,919
Speaker 10: Actually Biden is a matter of president for business. Even

1139
00:52:18,960 --> 00:52:21,279
Speaker 10: though the regulatory thing won't be exactly what.

1140
00:52:21,360 --> 00:52:22,880
Speaker 7: You want and we'll have to work on it. But

1141
00:52:23,080 --> 00:52:23,840
Speaker 7: rule of laws.

1142
00:52:23,680 --> 00:52:26,359
Speaker 4: Fundamental in your perspective, your point of view.

1143
00:52:26,600 --> 00:52:28,840
Speaker 7: Yes, I'm not yes, this is, but I do wonder.

1144
00:52:28,760 --> 00:52:30,720
Speaker 3: That you know soon as you look at the November

1145
00:52:30,800 --> 00:52:34,680
Speaker 3: election that outcome if it is not another of Biden

1146
00:52:34,760 --> 00:52:38,320
Speaker 3: White House present a Trump former presidents come back in

1147
00:52:38,360 --> 00:52:39,720
Speaker 3: the White House, what does.

1148
00:52:39,600 --> 00:52:41,600
Speaker 4: It mean for the tech industry or your or your

1149
00:52:41,680 --> 00:52:44,120
Speaker 4: group in terms of the rule of law is not followed.

1150
00:52:44,360 --> 00:52:46,560
Speaker 10: Well, so look, I think it's the tech industry can

1151
00:52:46,719 --> 00:52:48,879
Speaker 10: tribe under either administration.

1152
00:52:49,880 --> 00:52:51,319
Speaker 7: But I do think that if.

1153
00:52:52,800 --> 00:52:55,480
Speaker 10: Either Biden or Trump. But if I think Trump's elected,

1154
00:52:55,520 --> 00:52:57,800
Speaker 10: I think history will look back as the beginning of

1155
00:52:57,880 --> 00:53:00,759
Speaker 10: the end of the American world order, and that will

1156
00:53:00,840 --> 00:53:02,960
Speaker 10: have a massive effect on global business.

1157
00:53:03,400 --> 00:53:05,720
Speaker 2: How far are you willing to go to to reelect

1158
00:53:05,719 --> 00:53:06,240
Speaker 2: your bodies.

1159
00:53:06,719 --> 00:53:10,080
Speaker 10: Well, I'm willing to invest, I'm willing to speak, I'm

1160
00:53:10,080 --> 00:53:13,840
Speaker 10: willing to campaign, I'm willing to try to. Like when

1161
00:53:13,880 --> 00:53:16,560
Speaker 10: a prominent business leader speaks up and says I'm pro drop,

1162
00:53:16,800 --> 00:53:17,759
Speaker 10: I call them and I.

1163
00:53:17,760 --> 00:53:18,600
Speaker 7: Said, let's talk about it.

1164
00:53:18,760 --> 00:53:19,799
Speaker 9: Do you ever change their mind?

1165
00:53:20,239 --> 00:53:22,840
Speaker 7: Of course? Look for example, Look, one of things you

1166
00:53:22,920 --> 00:53:24,240
Speaker 7: have to do is think about blind spots.

1167
00:53:24,280 --> 00:53:26,960
Speaker 10: Like I just said something that was positive about how

1168
00:53:27,120 --> 00:53:28,760
Speaker 10: Trump's administration was running.

1169
00:53:28,840 --> 00:53:30,920
Speaker 7: Like I inspite to say you want a new regulation,

1170
00:53:31,040 --> 00:53:33,279
Speaker 7: we're moving old one. It's an exactly right thing.

1171
00:53:33,560 --> 00:53:34,239
Speaker 4: But I think what you.

1172
00:53:34,280 --> 00:53:38,080
Speaker 3: Said about globally the perception of the rest of the

1173
00:53:38,160 --> 00:53:41,360
Speaker 3: world potentially what the US means and what it is

1174
00:53:41,440 --> 00:53:41,840
Speaker 3: in terms of.

1175
00:53:42,680 --> 00:53:43,480
Speaker 4: That's a bigger story.

1176
00:53:43,719 --> 00:53:47,560
Speaker 10: People will not trust us live. They will say, now

1177
00:53:47,680 --> 00:53:49,680
Speaker 10: no longer trust that. You're into the world of law,

1178
00:53:49,840 --> 00:53:51,279
Speaker 10: an equal system, all the rest.

1179
00:53:51,400 --> 00:53:54,160
Speaker 3: All right, if you weren't in a world where Jennai

1180
00:53:54,480 --> 00:53:56,360
Speaker 3: was everything twenty seconds.

1181
00:53:56,440 --> 00:53:58,200
Speaker 4: What's the other technology we should have it on?

1182
00:53:58,400 --> 00:54:00,759
Speaker 10: Radars don't work and career well, so the other thing

1183
00:54:00,800 --> 00:54:03,160
Speaker 10: that's fallen behind it is synthetic biology. And when you

1184
00:54:03,239 --> 00:54:05,360
Speaker 10: put them together, by the way, like the invention of

1185
00:54:05,480 --> 00:54:09,440
Speaker 10: new pharmaceuticals, new medicines, everything else, it's like, let's just

1186
00:54:09,560 --> 00:54:11,680
Speaker 10: hold on to get to the future, because the future

1187
00:54:11,719 --> 00:54:12,600
Speaker 10: can be so amazing.

1188
00:54:13,040 --> 00:54:14,560
Speaker 4: Do you think about what a I could do? Right,

1189
00:54:14,640 --> 00:54:19,040
Speaker 4: it's just play around. Thank you so much, Thank you

1190
00:54:19,120 --> 00:54:23,359
Speaker 4: for enduring my pleasure. Appreciate it. Read, Thank you so much.

1191
00:54:23,880 --> 00:54:24,640
Speaker 9: That's read.

1192
00:54:24,680 --> 00:54:33,360
Speaker 2: Hoffman of Paypo linked New York Times best selling author

1193
00:54:33,760 --> 00:54:37,960
Speaker 2: his podcasts The Possible Podcast Season two started. It's out

1194
00:54:38,040 --> 00:54:38,719
Speaker 2: No love is.

1195
00:54:38,760 --> 00:54:40,560
Speaker 4: Love It all right, folks who are listening, row to

1196
00:54:40,640 --> 00:54:41,560
Speaker 4: Bloomberg Business Week.

1197
00:54:41,560 --> 00:54:44,480
Speaker 3: We're live at the Bloomberg Technology Summer in San Francisco.

1198
00:54:45,080 --> 00:54:49,680
Speaker 1: This is the Bloomberg Business Week podcast, available on Apple, Spotify,

1199
00:54:49,880 --> 00:54:53,560
Speaker 1: and anywhere else you get your podcast. Listen live weekday

1200
00:54:53,600 --> 00:54:57,040
Speaker 1: afternoons from two to five pm Eastern, on Bloomberg dot Com,

1201
00:54:57,280 --> 00:55:00,680
Speaker 1: the iHeartRadio app, tune In, and the Bloomberg Business. You

1202
00:55:00,760 --> 00:55:03,840
Speaker 1: can also watch us live every weekday on YouTube and

1203
00:55:04,120 --> 00:55:05,800
Speaker 1: always on the Bloomberg terminal.